Integrating Local Material Recognition with Large-Scale Perceptual Attribute Discovery

04/05/2016
by   Gabriel Schwartz, et al.
Drexel University
0

Material attributes have been shown to provide a discriminative intermediate representation for recognizing materials, especially for the challenging task of recognition from local material appearance (i.e., regardless of object and scene context). In the past, however, material attributes have been recognized separately preceding category recognition. In contrast, neuroscience studies on material perception and computer vision research on object and place recognition have shown that attributes are produced as a by-product during the category recognition process. Does the same hold true for material attribute and category recognition? In this paper, we introduce a novel material category recognition network architecture to show that perceptual attributes can, in fact, be automatically discovered inside a local material recognition framework. The novel material-attribute-category convolutional neural network (MAC-CNN) produces perceptual material attributes from the intermediate pooling layers of an end-to-end trained category recognition network using an auxiliary loss function that encodes human material perception. To train this model, we introduce a novel large-scale database of local material appearance organized under a canonical material category taxonomy and careful image patch extraction that avoids unwanted object and scene context. We show that the discovered attributes correspond well with semantically-meaningful visual material traits via Boolean algebra, and enable recognition of previously unseen material categories given only a few examples. These results have strong implications in how perceptually meaningful attributes can be learned in other recognition tasks.

READ FULL TEXT VIEW PDF
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 5

page 6

page 7

01/09/2018

Recognizing Material Properties from Images

Humans rely on properties of the materials that make up objects to guide...
11/28/2016

Material Recognition from Local Appearance in Global Context

Recognition of materials has proven to be a challenging problem due to t...
08/25/2021

Improving Object Detection and Attribute Recognition by Feature Entanglement Reduction

We explore object detection with two attributes: color and material. The...
06/13/2018

An intuitive control space for material appearance

Many different techniques for measuring material appearance have been pr...
01/07/2021

The joint role of geometry and illumination on material recognition

Observing and recognizing materials is a fundamental part of our daily l...
12/14/2020

Deep Learning for Material recognition: most recent advances and open challenges

Recognizing material from color images is still a challenging problem to...
10/02/2017

Redefining A in RGBA: Towards a Standard for Graphical 3D Printing

Advances in multimaterial 3D printing have the potential to reproduce va...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.

1 Introduction

Figure 1: Existing attempts to leverage attributes for material recognition have recognized them separately from material categories. These approaches are inherently incompatible with large-scale recognition, from which attributes may be extracted, as semantic annotation of material attributes is challenging. In this paper, we show that we can automatically discover discriminative and semantically meaningful, perceptually-motivated, material attributes inside a local material recognition network trained end-to-end for category recognition.

Attributes have proven to be a valuable intermediate representation for higher-level image understanding tasks. Material attributes, attributes that encode unique visual and non-visual material properties, are particularly useful as they provide a discriminative representation for materials whose appearance otherwise exhibits large intra-class variation [15]. Beyond just suggesting the presence of various materials, material attributes can inform us as to the potential physical properties, such as “rough” or “soft”, a material might exhibit. These cues can, for instance, guide autonomous interaction with real-world surfaces made of various materials. Attributes, in general, also have the desirable property that they can form a compact representation for unseen categories from few examples (N-shot learning).

Existing material category and attribute recognition methods consider attributes separately from category recognition. Attributes are used either solely as an intermediate representation [15], or as an automatically discovered perceptual representation for the same purpose [16, 22]. In other words, material categories are defined on top of separately recognized material attributes. As a result, if both attributes and material recognition are required, images must pass through two separate processes.

We would like to take advantage of the benefits of end-to-end learning to incorporate automatically-discovered attributes with material recognition in one seamless process. Material attribute recognition, however, is not easily scalable. Past approaches rely on semantic attributes, such as “shiny” or “fuzzy”, that need careful annotation by a consistent annotator as their appearance may not be readily agreed upon. This precludes the use of large-scale crowdsourcing. We are also specifically interested in local material recognition: recognizing materials using only information from small patches inside object boundaries so as to separate materials from the surrounding objects. This enables the recognition of material regardless of its situational context (e.g., what it makes up as an object or the place in which it is found), which is essential for realizing recognition of materials in general context (e.g., recognizing ceramic without knowing that the object is a cup). Material recognition methods that rely on context like object shape fundamentally confuse objects and materials: when that context is not available, their accuracy suffers [15, 16].

In this paper, we realize large-scale end-to-end learning for local material recognition and show that perceptual material attributes (e.g., “smooth” and “shiny”) can be extracted from the same framework. As depicted in Fig. 1, We introduce a novel material attribute-category CNN architecture (MAC-CNN) to show that perceptual material attributes, recognizable at the local level, can be discovered during material recognition. By introducing additional auxiliary attribute layers (layers connected to the network but not participating in the classification loss) and constraints derived from human material perception, we find that we may discover perceptual material attributes inside a material recognition framework. Unlike methods that rely on images and text (along with material annotations), we require weak supervision consisting only of a perceptual distance matrix of material categories to discover the attributes.

As part of our work, we also introduce a novel local material image database. Despite the importance of local material recognition as demonstrated in [15, 16], existing material databases have been tailored for global material recognition based on large image patches or whole images that inevitably mix object appearance with material appearance. Local image patches can be extracted from the Flickr Materials Database [19], but the use of only Flickr images biases the dataset towards more artistic or professional images. Recent datasets, such as the Materials in Context (MINC) dataset [3], take steps to address this, but have inconsistencies in the definition of what makes a material category (e.g., “mirror” and “carpet” which are obviously objects are used as materials). The patches they extract are also large enough to include entire objects, further confusing the recognition of objects and materials. Fig. 10 of [3] clearly shows that objects are recognized to identify materials (e.g., “mirror” as a material is recognized by finding actual mirrors and “fabric” is recognized by finding pillows). In contrast, we introduce the first comprehensive large-scale database explicitly targeted at local material recognition. We derive a systematically organized hierarchy for material categories, and we collect annotations for images from a wide variety of sources while carefully ensuring that object information, such as shape, is not present.

Interesting parallels can be found in recent neuroscience studies that reveal that human material perception produces an internal representation corresponding to semantic material attributes. Hiramatsu [8] and Goda [7] have investigated how visual information is transformed in the brain during the human and primate recognition of materials. They find that the material representation in our visual system shifts from raw image features at lower levels (V1/V2) to perceptual properties (such as matte, colorful, fuzzy, shiny, etc.) in higher-level brain regions dedicated to recognition (FG/CoS). On the other hand, in computer vision, specifically in the separate domain of conventional object recognition, Zhou [25]

find that object detectors appear in scene recognition CNNs. Our work serves as further support for the idea of semantically meaningful attributes arising inside category recognition process, by showing that inherent material attributes can be made explicit inside a material recognition process.

Our results show that MAC-CNN produces a generalizable internal material representation. We show that the attributes we extract exhibit the same properties, such as spatial consistency, as existing automatically-discovered perceptual material attributes. By visualizing the arrangement of material categories in the space of attribute probabilities, we show that attributes separate materials into distinct clusters. We perform true local material recognition, predicting categories for single small image patches with no aggregation, a significantly more challenging task than previous approaches. While previous work suggests that perceptual attributes are correlated with manually-identified semantic material traits, such as “fuzzy” or “smooth”, we are the first to conclusively demonstrate this by recognizing them solely from our extracted attributes with logic regression. Finally, we demonstrate that the extracted material attributes add significant information to recognize previously unseen material categories from a small number of training examples (i.e., N-shot learning with material attributes). These results show that our method successfully extracts effective and semantically meaningful internal representations of complex material appearance from a local material recognition network. These results also suggest a general approach for extracting semantically meaningful, perceptually-motivated attributes in general recognition processes, such as object and place recognition.

2 Related Work

In this paper, we investigate convolutional neural networks (CNNs) as the framework within which we should find perceptual material attributes. In the past, for recognition tasks other than materials such as object and places, research on localizing attributes inside CNNs for category recognition has been explored. Specifically for object attributes and categories, Shankar [17] recently proposed a modified training procedure called “deep carving” which provides the CNN with attribute pseudo-label targets, updated periodically during training. This causes the resulting network to be better-suited for object attribute prediction. Escorcia [4] showed that known semantic object attributes can be extracted from a CNN. Similar to our work, they showed that object attributes depend on features in all layers of the CNN. ConceptLearner, proposed by Zhou [24] uses weak supervision, in the form of images with associated text content, to discover semantic place attributes that can be interpreted as object descriptions. These attributes correspond to terms within the text that appear in the images. All of these frameworks predict a single set of object or place attributes for an entire image, as opposed to the per-pixel material attributes discussed in our work. Furthermore, our extracted attributes do not require semantic information (which may be challenging to collect in a consistent manner), and are defined based on human perceptual information.

At the intersection of neuroscience and computer vision, Yamins [23]

find that feature responses from high-performing CNNs can accurately model the neural response of the human visual system in the inferior temporal (IT) cortex. They perform a linear regression from CNN feature outputs to IT neural response measurements and find that the CNN features are good predictors of neural responses. Their work focuses on object recognition CNNs, not materials. Hiramatsu 

[8] take functional magnetic resonance imaging (fMRI) measurements and investigate their correlation with both direct visual information and perceptual material properties (similar to the material traits of [15]) at various areas of the human visual system. They find that pairwise material dissimilarities derived from fMRI data correlate best with direct visual information (analogous to pixels) at the lower-order areas and with perceptual attributes at higher-order areas. Goda [7] obtain similar findings in non-human primates. Of particular importance is the fact that their work inherently considers materials independently from objects. Material samples are shown with the same cylinder shapes, thus avoiding any distracting object cues. These studies suggest the existence of perceptual material attributes in human local material recognition.

Our work is closely related to the non-semantic perceptual material attributes discovered by Schwartz and Nishino [16]. In their work, they collect measurements of human perceptual distances between material categories and use those distances to discover perceptual material attributes that reproduce these distances. These attributes are subsequently used to recognize material categories. We use the constraints derived in their work as a basis for our auxiliary attribute layers. This approach can be considered similar to the work of Lee [10], which introduced “deep supervision” via auxiliary loss functions to better-propagate gradient information during CNN training for object recognition. They do so by adding additional SVM-like loss functions that encourage classification at lower levels of the network. Rather than simply replicating the final classification loss, we impose new constraints to explicitly output additional information about the input, in our case the perceptual material attributes.

3 Perceptual Material Attributes from Local Material Recognition

In this paper, we show that perceptual material attributes can be integrated with a local material recognition framework and output as a side-product. We find the human-perception-based attributes of Schwartz and Nishino [16] to be particularly relevant, as they automatically discover material attributes from weak supervision. Their attributes are, however, recognized separately from materials in a slow process that scales poorly with more training data. In this section we derive a novel framework to discover perceptual attributes similar to those in [16]

, inside a CNN framework, while simultaneously learning to classify materials.

A straightforward approach to integrating material attributes and category recognition would be to add an attribute prediction layer at the top of a material recognition CNN, immediately before the final material category probability softmax layer. As an initial investigation, we implemented this approach with the goal of predicting the perceptual attributes derived from 

[16]. We, however, found that constraining the network in such a fashion results in either poorly-recognized attributes or categories.

These results suggest that materials are not defined simply by their attributes. This agrees with the findings of Hiramatsu [8], where they note that the human neural representation of material categories transitions from visual (raw image features) to perceptual (visual properties like “shiny”) in an hierarchical fashion. This also suggests that material attributes require information from multiple levels of the material recognition network.

3.1 Material Attribute-Category CNN

Figure 2: Material Attribute-Category CNN (MAC-CNN) Architecture: We introduce auxiliary fully-connected attribute layers to each spatial pooling layer and combine the per-layer predictions into a final attribute output via an additional set of weights. The loss functions attached to the attribute layers encourage the extraction of attributes that match the human material representation encoded in perceptual distances. The first set of attribute layers acts as a set of weak learners to extract attributes wherever they are present. The final layer combines them to form a single prediction.

We need a means of extracting attribute information at multiple levels of the network. Simply combining all feature maps from all network layers and using them to predict attributes would be computationally impractical. Rather than directly using all features at once, we augment an initial CNN designed for material classification with a set of auxiliary fully-connected layers attached to the spatial pooling layers. This allows the attribute layers to use information from multiple levels of the network without needing direct access to every feature map. We treat the additional layers as a set of weak learners, each auxiliary layer discovering the attributes available at the corresponding level of the network. This concept is similar to deep supervision by Lee [10]. Their goal, however, is to inject the category recognition loss function into intermediate layers for better end recognition (in their case, object recognition) by simply propagating the same classification targets (via SVM-like loss functions) to the lower layers. Our goal is to discover and extract perceptual material attributes through this internal supervision using loss functions different from that for material category recognition.

For the auxiliary layer loss functions, we extend the perceptual attribute loss functions of [16] and apply them to the outputs of each auxiliary fully-connected layer. Schwartz and Nishino’s proposed method begins with a set of pairwise perceptual distances between material categories measured via human yes/no binary similarity annotations on material image patches. From these distances, they learn a mapping matrix between categories and unknown, non-semantic attributes. The mapping preserves the pairwise human perceptual distances while causing the resulting attributes to exhibit the behaviors, such as spatial consistency, of semantic attributes. We derive our attribute layer loss functions from these learning constraints.

Specifically, assuming the output of a given pooling layer in the network for image is , and given categories and a set of sample points

for density estimation, we add these auxiliary loss functions:

(1)
(2)

where clamps the outputs within to conform to attribute probabilities, and weights represent the auxiliary fully-connected layers we add to the network. represents a row in the category-attribute mapping matrix we derived from our data by collecting the yes/no similarity answers used in [16] for patches in our database (see Sec. 4). Equation 1 causes the attribute layer to discover attributes which match the perceptual distances measured from human annotations. As certain attributes are expected to appear at different levels of the network, some layers will be unable to extract them. This implies that their error should be sparse, either predicting an attribute well or not at all. For this reason we use an L1 error norm. Equation 2

, applied only to the final attribute layer, encourages the distribution of the attributes to match those of known semantic material traits. It takes the form of a KL-divergence between a Beta distribution (empirically observed by 

[16]

to match the distribution of semantic attribute probabilities), and a Kernel Density Estimate

of the extracted attribute probability sampled at points .

The reference network we build on is based on the high-performing VGG-16 network of Simonyan and Zisserman [20]. We use their trained convolutional weights as initialization where applicable, and add new fully-connected layers for material classification. Fig. 2 shows our architecture for material attribute discovery and category recognition. We refer to this network as the Material Attribute-Category CNN (MAC-CNN).

4 Local Material Database

In order to train the category recognition portion of the MAC-CNN, we need a proper local material recognition dataset. We find existing material databases lacking in a few key areas necessary to properly perform local material recognition. Previous material recognition datasets [18, 2, 3] have relied on ad-hoc choices regarding the selection and granularity of material categories (e.g., carpet and wall are considered materials). When patches are involved, as in [3], the patches can be as large as 24% of the image size surrounding a single pixel identified as corresponding to a material. These patches are large enough to include entire objects. These issues make it difficult to separate challenges inherent to material recognition from those related to general recognition tasks and inevitably lead to material recognition based on object and scene information, which would not be beneficial for scene understanding tasks (e.g., recognized material information will not help recognize objects and places as it already relies on the recognition of them). We also find that image diversity is still lacking in modern datasets: FMD [18] is solely sourced from Flickr which is heavily biased towards professional photography and MINC [2] is predominantly sourced from professional real-estate photography. We introduce a new local material recognition dataset to support the experiments in this paper.

4.1 Material Category Hierarchy

Figure 3: Local material patches extracted as the final step in our database creation process. These patches are used to compute human perceptual distances, and also form the training input for our combined material attribute-category CNN.

Material categories in existing datasets have been selected in a rather adhoc manner in the past. Examples of this issue include the proposed material categories “mirror” (actually an object), and “brick” (an object or group of objects). Existing categories also confuse materials and their properties (e.g., surface finish), for example, separating “stone” from “polished stone”. To address the issue of material category definition, we propose a more carefully-selected set of material categories for local material recognition. We derive a taxonomy of materials based on canonical categorization in materials science [1] and create a hierarchy based on the generality of each material family. Please see our supplemental material for a complete diagram of the hierarchy including all categories at all levels.

Our hierarchy consists of a set of three-level material trees. The highest level corresponds to major structural differences between materials in the category. Metals are conductive, polymers are composed of long chain molecules, ceramics have a crystalline structure, and composites are fusions of materials either bonded together or in a matrix. We define the mid-level (whcih can also be referred to as entry-level [12]) categories as groups that separate materials based primarily on their visual properties. Rubber and paper are flexible, for example, but paper is generally matte and rubber exhibits little color variation. The lowest level, fine-grained categories, can often only be distinguished via a combination of physical and visual properties. Silver and steel, for example, may be challenging to distinguish based solely on visual information.

Such a hierarchy is sufficient to cover most natural and manmade materials. In creating our hierarchy, however, we found that certain categories that are in fact materials did not fit within the strict definitions described above. For the sake of completeness, we make the conscious decision to add these mid-level categories to our data collection process. These categories are: food, water, and non-water liquids. While food is both a material and an object, we rely on our annotation process (Sec. 4.2) to ensure we obtain examples of the former and not the latter.

4.2 Data Collection and Annotation

Figure 4: Annotators did not hesitate to take advantage of the ability to draw multiple regions, and most understood the guidelines concerning regions crossing object boundaries. As a result, we have a rich database of segmented local material regions.

The mid-level set of categories forms the basis for a crowdsourced annotation pipeline to obtain material regions from which we may extract local material patches (Fig. 3). We employ a multi-stage process to efficiently extract both material presence and segmentation information for a set of images. The first stage asks annotators to identify materials present in the image. Given a set of images with materials identified in each image, the second stage presents annotators with a user interface that allows them to draw multiple regions in an image. Each annotator is given a single image-material pair and asked to mark regions where that material is present. While not required, our interface allows users to create and modify multiple disjoint regions in a single image. Images undergo a final validation step to ensure no poorly drawn or incorrect regions are included.

Each image in the first stage is shown to multiple annotators and a consensus is taken to filter out unclear or incorrect identifications. While sentinels and validation were not used to collect segmentations in other datasets, ours is intended for local material recognition. This implies that identified regions should contain only the material of interest. During collection, annotators are given instructions to keep regions within object boundaries, and we validate the final image regions to insure this.

Image diversity is an issue present to varying degrees in current material image datasets. The Flickr Materials Database (FMD) [19] contains images from Flickr which, due to the nature of the website, are generally more artistic in nature. The OpenSurfaces and Materials in Context datasets [2, 3] attempt to address this, but still draw from a limited variety of sources (e.g., real estate photographs). We source our images from multiple existing image datasets spanning the space of indoor, outdoor, professional, and amateur photographs. We use images from the PASCAL VOC database [5], the Microsoft COCO database [11], the FMD [19]

, and the ImageNet database 

[14].

Figure 5: Attribute Space Embedding via t-SNE [21]: Many categories, such as water, food, foliage, soil, and wood, are very well-separated in the attribute space. We find that this separation corresponds roughly with per-category accuracy.

Examples in Fig. 4 show that our annotation pipeline successfully provides properly-segmented material regions within many images. Many images also contain multiple regions. While the level of detail for provided regions varies from simple polygons to detailed material boundaries, the regions all contain single materials. The final database contains 2669 images with associated material segmentations. We may extract at least 200,000 image patches of decent size (e.g., ) from inside the segmented regions without crossing object boundaries from this database. The database and the code for MAC-CNN will be made publicly accessible after publication.

5 Perceptual Attributes in the MAC-CNN

Figure 6: Each column after the first (the input image) shows per-pixel probabilities for an extracted perceptual attribute. The attributes form clearly delineated regions, similar to semantic attributes, and their distributions match as well.

To verify that the perceptual attributes we seek can in fact be extracted with our MAC-CNN, we augment our dataset with annotations to compute the necessary perceptual distances described in [16]. Using our dataset and these distances, we derive a category-attribute matrix and train an implementation of the MAC-CNN described in Sec. 3.1.

We train the network on ~200,000

image patches extracted from segmented material regions. Optimization is performed using mini-batch stochastic gradient descent with momentum. The learning rate is decreased by a factor of 10 whenever the validation error increases, until the learning rate falls below

.

5.1 Perceptual Material Attribute Properties

We examine the properties of our perceptual material attributes by visualizing how they separate materials, computing per-pixel attribute maps to verify that the attributes are being recognized consistently, and linking the non-semantic attributes with known semantic material traits (“fuzzy”, “smooth”, etc…) to visualize semantic content. Figs. 5, 6, and 7 are generated using a test set of held-out images.

A 2D embedding of material image patches shows that the perceptual attributes (Fig. 5) separate material categories. A number of materials are almost completely distinct in the attribute space, while a few form overlapping but still distinguishable regions. Foliage, food, and water form particularly clear clusters. The quality of the clusters matches the per-category recognition rates, with accurately-recognized categories forming more separate clusters.


  Manmade   Organic   Rough

  Smooth   Striped   Soft

  Metallic   Organic   Smooth

 Shiny  Organic  Smooth

 Fuzzy  Organic  Smooth
Figure 7: By performing logic regression from our MAC-CNN extracted attributes to material traits, we are able to extract semantic information from our non-semantic attributes. Doing so in a sliding window gives per-pixel semantic material trait information. The predictions show crisp regions that correspond well with their associated semantic traits. Traits are independent, and thus the maps contain mixed colors. Fuzzy and organic in the lower right image, for example, creates a yellow tint. These semantic material traits computed from discovered material attributes provide rich information about the underlying surface properties that can be leveraged to determine how to interact with them.

Visualizations of per-pixel attribute probabilities in Fig. 6 show that the attributes are spatially consistent. While overfitting is difficult to measure for weakly-supervised attributes, we use spatial consistency as a proxy. Spatial consistency is an indicator that the attributes are not overly-sensitive to minute changes in local appearance, something that would appear if overfitting were present. The attributes exhibit correlation with the materials that induced them: attributes with a strong presence in a material region in one image often appear similarly in others. The visualizations also clearly show that the attributes are representing more than trivial properties such as “flat color” or “textured”.

Logic regression [13] is a method for building trees that convert a set of boolean variables into a probability value via logical operations (AND, OR, NOT). It is well-suited for collections of binary attributes such as ours. Results of performing logic regression (Fig. 7) from extracted attribute predictions to known semantic material traits (such as fuzzy, shiny, smooth etc…) show that our MAC-CNN attributes encode material traits with the same average accuracy (75%) as the attributes of [16]. For per-trait accuracy comparisons, please see our supplemental materials. We may also predict per-pixel trait probabilities in a sliding window fashion, showing that the attributes are encoding both perceptual and semantic material properties. The material attributes provide rich information regarding the surface properties that may benefit, for instance, action planning for autonomous agents.

5.2 Local Material Recognition

Figure 8: These material maps, obtained by applying the MAC-CNN in a sliding window, show that we may obtain coherent regions using only small local patches as input. The foliage predictions on the couch are reasonable, as the local appearance pattern is indeed a flower. In the baseball image, the local appearance of the fence resembles lace (a fabric).

To evaluate the material recognition portion of the MAC-CNN output, we compute local material recognition accuracy using the MAC-CNN trained on our database. Accuracy is measured as the average number of correct patch category predictions. Average local material accuracy is 60.2% across all categories. Foliage is the most accurately recognized, consistent with past material recognition results in which foliage is the most visually-distinct category. Paper is the least well-recognized category. Unlike the artistic closeup images of the FMD, many of the images in our database come from ordinary images of scenes. Paper, in these situations, shares its appearance with a number of other materials such as fabric. These results can be viewed as a baseline accuracy for this dataset using a VGG architecture trained with small patches. It is important to note that we are recognizing materials directly from single small image patches, with none of the region-based aggregation or large patches used in [15, 16, 3]. This is a much more challenging task as the available information is restricted. For a breakdown of per-category accuracy, please see our supplemental material.

Figure 9: Graphs of unseen category recognition accuracy vs. training set size for various held-out categories. The rapid plateau shows that we need only a small number of examples to define a previously-unseen category. The accuracy difference between feature sets shows that the attributes are contributing significant novel information. Even when the attributes do not outperform material probabilities on their own, the combination is still superior demonstrating the rich discriminative information carried by the extracted material attributes.

If perceptual material attributes are present in the material classification network, we must be able to extract them without compromising the network’s ability to recognize materials. We compare local material recognition accuracy with and without the auxiliary attribute loss functions to verify this. We find that the average material category accuracy does not change when the attribute layers are removed. While the attribute layers are auxiliary, they are connected to spatial pooling layers at every level and thus the attribute constraints affect the entire network. If the attributes were not in fact encoding visual material properties, constraining the network to extract them would negatively affect the material recognition performance.

A full semantic segmentation framework is beyond the scope of this paper. We are, however, able to use the same attribute/material CNN to produce per-pixel material probability predictions. Results in Fig. 8 show that we may still generate reasonable material probability maps even from purely local information.

6 Unseen Material Category Recognition

One prominent application of attributes is in unseen category recognition tasks. Examples of these tasks include one-shot [6] or zero-shot learning [9]. Zero-shot learning allows recognition of a unseen category from a human-supplied list of applicable semantic attributes. Since our attributes are non-semantic, zero-shot learning is not applicable here. We may, however, investigate the generalization of our attributes through a form of N-shot learning in which we use image patches extracted from a very small number of images to learn an unseen category. While materials have been used in the past as attributes for zero- or one-shot learning, we show that the perceptual attributes of those materials are discriminative enough to recognize previously-unseen materials given only a few examples.

To evaluate unseen category recognition from perceptual material attributes, we train a set of MAC-CNNs on modified datasets where each is missing all examples of a single held-out category. No examples of that category are present during training. The corresponding row of the category-attribute matrix is also removed. The same number of attributes are defined based on the remaining categories.

For unseen category training, we show that we require only a very small set of examples to recognize an unseen category. We train a simple linear binary SVM to distinguish between the previously-seen training categories and the held-out category based on their discovered attribute probabilities, computed on patches extracted from each input image. We measure the effectiveness of unseen category recognition by the fraction of final held-out category samples properly identified as belonging to that category. As a baseline, we use the material probability outputs from the MAC-CNN as a feature instead of attributes.

Fig. 9 shows plots of unseen category recognition effectiveness as the number of training examples for the held-out category varies. We can see that the accuracy plateaus quickly, indicating that the attributes provide a compact and accurate representation for novel material categories. The number of images we are required to extract patches from to obtain reasonable accuracy is generally quite small (on the order of 10) compared to full material category recognition frameworks which require hundreds of examples. Furthermore, we include accuracy for the same predictions based on only material probabilities instead of attribute probabilities, as well as using a concatenation of both. Attributes alone offer better recognition for some unseen categories. Even when they do not, the addition of attributes still increases performance. This clearly shows that the extracted attributes can expose discriminative information that would not ordinarily be available.

7 Conclusion

We have proposed a single framework that integrates weakly-supervised attribute discovery with local material recognition. Our proposed CNN architecture allows us to discover perceptual material attributes within a local material recognition network. To evaluate the framework, and to address issues present in existing material recognition databases, we built a new material image database from carefully-chosen material categories. The accuracy of unseen category recognition based solely on our discovered attributes and few sample images shows that the attributes form a compact representation for novel materials.

We find the parallels between our own human visual perception of materials and the material attributes discovered in the MAC-CNN architecture particularly interesting. Our integration of attribute and category recognition with a single network likely has implications in other tasks such as object and scene recognition, and we may find similar parallels there as well.

Acknowledgments

This work was supported by the Office of Naval Research grant N00014-16-1-2158 (N00014-14-1-0316) and N00014-17-1-2406, and the National Science Foundation award IIS-1421094. The Titan X used for part of this research was donated by the NVIDIA Corporation.

References

  • [1] Matbase: Chemical, Mechanical, Physical and Environmental Properties of Materials. http://www.matbase.com.
  • [2] S. Bell, P. Upchurch, N. Snavely, and K. Bala. OpenSurfaces: A Richly Annotated Catalog of Surface Appearance. In ACM Transactions on Graphics (SIGGRAPH 2013), 2013.
  • [3] S. Bell, P. Upchurch, N. Snavely, and K. Bala. Material Recognition in the Wild with the Materials in Context Database. In CVPR, 2015.
  • [4] V. Escorcia, J. C. Niebles, and B. Ghanem. On the Relationship between Visual Attributes and Convolutional Networks. In CVPR, pages 1256–1264, 2015.
  • [5] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88(2):303–338, June 2010.
  • [6] L. Fei-Fei, R. Fergus, and P. Perona. One-Shot Learning of Object Categories. TPAMI, 28(4):594–611, 2006.
  • [7] N. Goda, A. Tachibana, G. Okazawa, and H. Komatsu. Representation of the Material Properties of Objects in the Visual Cortex of Nonhuman Primates. The Journal of Neuroscience, 34(7):2660–2673, 2014.
  • [8] C. Hiramatsu, N. Goda, and H. Komatsu. Transformation from Image-Based to Perceptual Representation of Materials along the Human Ventral Visual Pathway. NeuroImage, (57):482–494, 2011.
  • [9] C. Lampert, H. Nickisch, and S. Harmeling. Learning to Detect Unseen Object Classes by Between-Class Attribute Transfer. In CVPR, pages 951–958, 2009.
  • [10] C.-Y. Lee, S. Xie, P. W. Gallagher, Z. Zhang, and Z. Tu. Deeply-Supervised Nets. In AISTATS, pages 562–570, 2015.
  • [11] T.-Y. Lin, M. Marie, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick. Microsoft COCO: Common Objects in Context. In ECCV, 2014.
  • [12] V. Ordonez, J. Deng, Y. Choi, A. C. Berg, and T. L. Berg. From Large Scale Image Categorization to Entry-Level Categories. In ICCV, 2013.
  • [13] I. Ruczinski, C. Kooperberg, and M. LeBlanc. Logic Regression. Journal of Computational and Graphical Statistics, 12(3):475–511, 2003.
  • [14] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), 115(3):211–252, 2015.
  • [15] G. Schwartz and K. Nishino. Visual Material Traits: Recognizing Per-Pixel Material Context. In Color and Photometry in Computer Vision (Workshop held in conjunction with ICCV’13), 2013.
  • [16] G. Schwartz and K. Nishino. Automatically Discovering Local Visual Material Attributes. In CVPR, pages 3565–3573, 2015.
  • [17] S. Shankar, V. K. Garg, and R. Cipolla. DEEP-CARVING : Discovering Visual Attributes by Carving Deep Neural Nets. In CVPR, pages 3403–3412, 2015.
  • [18] L. Sharan, C. Liu, R. Rosenholtz, and E. H. Adelson. Recognizing Materials Using Perceptually Inspired Features. International Journal of Computer Vision, 2013.
  • [19] L. Sharan, R. Rosenholtz, and E. Adelson. Material Perception: What Can You See in a Brief Glance? Journal of Vision, 9(8):784, 2009.
  • [20] K. Simonyan and A. Zisserman. Very Deep Convolutional Networks for Large-Scale Image Recognition. In ICLR, pages 1–14, 2015.
  • [21] L. van der Maaten and G. Hinton. Visualizing Data using t-SNE. JMLR, 9:2579–2605, 2008.
  • [22] S. Vittayakorn, T. Umeda, K. Murasaki, K. Sudo, T. Okatani, and K. Yamaguchi. Automatic Attribute Discovery with Neural Activations. In ECCV, 2016.
  • [23] D. L. K. Yamins, H. Hong, C. F. Cadieu, E. A. Solomon, D. Seibert, and J. J. DiCarlo. Performance-optimized hierarchical models predict neural responses in higher visual cortex. PNAS, pages 8619–8624, 2014.
  • [24] B. Zhou, V. Jagadeesh, and R. Piramuthu. ConceptLearner: Discovering Visual Concepts from Weakly Labeled Image Collections. In CVPR, pages 1492–1500, 2015.
  • [25] B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba. Object Detectors Emerge in Deep Scene CNNs. In ICLR, 2015.