Learning Mid-Level Features and Modeling Neuron Selectivity for Image Classification

01/22/2014
by   Shu Kong, et al.
0

We now know that mid-level features can greatly enhance the performance of image learning, but how to automatically learn the image features efficiently and in an unsupervised manner is still an open question. In this paper, we present a very efficient mid-level feature learning approach (MidFea), which only involves simple operations such as k-means clustering, convolution, pooling, vector quantization and random projection. We explain why this simple method generates the desired features, and argue that there is no need to spend much time in learning low-level feature extractors. Furthermore, to boost the performance, we propose to model the neuron selectivity (NS) principle by building an additional layer over the mid-level features before feeding the features into the classifier. We show that the NS-layer learns category-specific neurons with both bottom-up inference and top-down analysis, and thus supports fast inference for a query image. We run extensive experiments on several public databases to demonstrate that our approach can achieve state-of-the-art performances for face recognition, gender classification, age estimation and object categorization. In particular, we demonstrate that our approach is more than an order of magnitude faster than some recently proposed sparse coding based methods.

READ FULL TEXT

page 7

page 8

page 14

page 15

page 16

page 17

page 18

page 19

research
08/29/2020

New feature for Complex Network based on Ant Colony Optimization for High Level Classification

Low level classification extracts features from the elements, i.e. physi...
research
11/12/2019

Random Projections of Mel-Spectrograms as Low-Level Features for Automatic Music Genre Classification

In this work, we analyse the random projections of Mel-spectrograms as l...
research
03/03/2021

Touchless Palmprint Recognition based on 3D Gabor Template and Block Feature Refinement

With the growing demand for hand hygiene and convenience of use, palmpri...
research
02/18/2023

An anatomy-based V1 model: Extraction of Low-level Features, Reduction of distortion and a V1-inspired SOM

We present a model of the primary visual cortex V1, guided by anatomical...
research
11/22/2016

Learning Multi-level Features For Sensor-based Human Action Recognition

This paper proposes a multi-level feature learning framework for human a...
research
11/13/2012

Deep Attribute Networks

Obtaining compact and discriminative features is one of the major challe...
research
08/10/2013

Learning Features and their Transformations by Spatial and Temporal Spherical Clustering

Learning features invariant to arbitrary transformations in the data is ...

Please sign up or login with your details

Forgot password? Click here to reset