Optimal Feature Selection in Biologically Inspired Model for Object Recognition Using Mutual Information Maximisation

Jazlaeiyan, Mohammad; Shahhoseini, Hadi Shahriar

doi:10.22041/ijbme.2014.14705

Optimal Feature Selection in Biologically Inspired Model for Object Recognition Using Mutual Information Maximisation

Document Type : Full Research Paper

Authors

Mohammad Jazlaeiyan ¹

Hadi Shahriar Shahhoseini ²

¹ M.Sc Graduate, Electronic Department, Faculty of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

² Associate Professor, Electronic Department, Faculty of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

https://doi.org/10.22041/ijbme.2014.14705

Abstract

Human visual system operates superior than best machine vision systems in object recognition. So, researchers in machine vision and neuroscience try to model human visual system in order to employ it in machine. HMAX is one of the best operating models in this area. It is based on the function of brain cells in the ventral stream of visual cortex and contains four computational layers. In the learning stage, many image partitions called image patches are extracted randomly with different sizes from training images. This random selection of image patches is one of the drawbacks of HMAX which decreases the performance and increases the computational complexity of the algorithm. In this paper, a novel patch selection from the set of random patches is proposed. In this method, using a recursive approach, optimal patches are selected from optimal features of training images by mutual information maximization feature selection. The performance of proposed algorithm in binary classification (existence or non-existence of objects in the images) is compared with HMAX and the superiority is proved.

Keywords

HMAX

Object Recognition

visual system

feature selection

Subjects

Biological Computer Modeling / Biological Computer Simulation

[1] D. H. Hubel T. N. Wiesel, “Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex” The Journal of physiology Vol 160, pp 106–154, 1962.

[2] D. H. Hubel, T. N Wiesel, “Receptive fields and functional architecture of monkey striate cortex” The Journal of physiology Vol 195, pp 215-243, 1968.

[3] M. Ghodrati, S. M. Khaligh Razavi, R. Ebrahimpour, K. Rajaei, M. Pooyan, “How Can Selection of Biologically Inspired Features Improve the Performance of a Robust Object Recognition Model?” PLOS ONE Vol 7, No 2, pp 1-12, 2012.

[4] K. Fukushima, “Neocognitron: A Self-Organizing Neural Network Model for A Mechanism of Pattern Recognition Unaffected by Shift in Position” BiologicalCybernetics Vol 36, No 4, pp 193–202, 1980.

[5] G. Wallis G, E. T. Rolls, “A Model of Invariant Object Recognition in the Visual System” Progress in Neurobiology Vol 51, pp 167-194, 1996.

[6] S. Grossberg, E. Mingolla, W. D. Ross, “Visual Brain and Visual Perception: How Does the Cortex Do Perceptual Grouping?” Trends in Neurosciences Vol 20, pp 106-111, 1997.

[7] S. Grossberg, “Towards A Unified Theory of Neocortex: Laminar Cortical Circuits for Vision and Cognition” Progress in Brain Research Vol 165, pp 79-104, 2007.

[8] M. Riesenhuber, T. Poggio, “Hierarchical Models of Object Recognition in Cortex” Nature Neuroscience Vol 2, No 11, pp 1019–1025, 1999.

[9] T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, T. Poggio, “Robust Object Recognition with Cortex-Like Mechanisms” IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 29, No 3, pp 411–426, 2007.

[10] K. Huang, T. Dacheng, Y. Yuan, L. Xuelong,
T. Tieniu, “Biologically Inspired Features for Scene Classification in Video Surveillance” IEEE Transactions on Systems Man and Cybernetics-Part B: Cybernetics Vol 41, No 1, pp 307-313, 2011.

[11] M. Hamidi, A. Borji, “Invariance Analysis of Modified C2 Features: Case Study-Handwritten Digit Recognition,” Machine Vision and Applications Vol 21, No 6, pp 969-979, 2010.

[12] C. Theriault, N. Thome, “Extended Coding and Pooling in the HMAX Model” IEEE Transaction on Image Processing Vol 22, No 2, 764-777, 2013.

[13] E. Krupka, A. Navot, N. Tishby, “Learning to Select Features using their Properties” Journal of Machine Learning Research Vol 9, No 10, pp 2349-2376, 2008.

[14] G. Brown, A. Pocock, M. J. Zhao, M. Lujan, “Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection” Journal of Machine Learning Research Vol 13, No 1, 2012.