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Image Classification with Classic and Deep Learning Techniques

05/11/2021
by   Òscar Lorente, et al.
13

To classify images based on their content is one of the most studied topics in the field of computer vision. Nowadays, this problem can be addressed using modern techniques such as Convolutional Neural Networks (CNN), but over the years different classical methods have been developed. In this report, we implement an image classifier using both classic computer vision and deep learning techniques. Specifically, we study the performance of a Bag of Visual Words classifier using Support Vector Machines, a Multilayer Perceptron, an existing architecture named InceptionV3 and our own CNN, TinyNet, designed from scratch. We evaluate each of the cases in terms of accuracy and loss, and we obtain results that vary between 0.6 and 0.96 depending on the model and configuration used.

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Code Repositories

Image-Classification

Image classifier using both classic computer vision techniques (Bag of Visual Words classifier using SVM) and deep learning techniques (MLPs, InceptionV3 and our own CNN, TinyNet).


view repo

MCV-M3-MLCV

Image classification - Bag of Visual Words, Keypoints and Descriptors, Spatial Pyramids, K-Means and GMM clustering, PCA, LDA, SVM, Fisher Vectors, Fine Tuning CNNs, Training CNNs from scratch.


view repo

MCV-M3-Machine-Learning-for-Computer-Vision

Image classifier using both classic computer vision techniques (Bag of Visual Words classifier using SVM) and deep learning techniques (MLPs, InceptionV3 and our own CNN, TinyNet).


view repo