Biological modeling of human visual system for object recognition using GLoP filters and sparse coding on multi-manifolds

Hierarchical MAX model (HMAX) is a bio-inspired model mimicking the visual information processing of visual cortex. However, the visual processing of lower level, such as retina and lateral geniculate nucleus (LGN), is not concerned, and the properties of higher-level neurons are not sufficiently specified. Given that, we develop an extended HMAX model, denoted as E-HMAX, by the following biologically plausible ways. First, contrast normalization is conducted on the input image to simulate the processing of human retina and LGN. Second, log-polar Gabor (GLoP) filters are used to simulate the properties of V1 simple cells instead of Gabor filters. Then, sparse coding on multi-manifolds is modeled to compute the V4 simple cell response instead of Euclidean distance. Meanwhile, a template learning method based on dictionary learning on multi-manifolds is proposed to select informative templates during template learning stage. Experimental results demonstrate that the proposed model has greatly outperformed the standard HMAX model. It is also comparable to some state-of-the-art approaches such as EBIM and OGHM-HMAX.

[1]  A. Mizuno,et al.  A change of the leading player in flow Visualization technique , 2006, J. Vis..

[2]  Peijie Yin,et al.  Biologically inspired model simulating visual pathways and cerebellum function in human - Achieving visuomotor coordination and high precision movement with learning ability , 2016, ArXiv.

[3]  Changsheng Xu,et al.  Exploiting Social-Mobile Information for Location Visualization , 2017, ACM Trans. Intell. Syst. Technol..

[4]  Narendra Ahuja,et al.  Learning recognition and segmentation of 3-D objects from 2-D images , 1993, 1993 (4th) International Conference on Computer Vision.

[5]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[6]  D J Field,et al.  Relations between the statistics of natural images and the response properties of cortical cells. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[7]  Timothée Masquelier,et al.  Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition , 2015, Scientific Reports.

[8]  S. Grossberg,et al.  A neural model of surface perception: lightness, anchoring, and filling-in. , 2006, Spatial vision.

[9]  E L Schwartz,et al.  Cortical Anatomy, Size Invariance, and Spatial Frequency Analysis , 1981, Perception.

[10]  Jianping Fan,et al.  iPrivacy: Image Privacy Protection by Identifying Sensitive Objects via Deep Multi-Task Learning , 2017, IEEE Transactions on Information Forensics and Security.

[11]  Jun Yu,et al.  Click Prediction for Web Image Reranking Using Multimodal Sparse Coding , 2014, IEEE Transactions on Image Processing.

[12]  Thomas Serre,et al.  A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex , 2005 .

[13]  Jun Yu,et al.  Pairwise constraints based multiview features fusion for scene classification , 2013, Pattern Recognit..

[14]  Bin Shen,et al.  Learning dictionary on manifolds for image classification , 2013, Pattern Recognit..

[15]  Jane You,et al.  HSAE: A Hessian regularized sparse auto-encoders , 2016, Neurocomputing.

[16]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[17]  J. Goo,et al.  Receiver Operating Characteristic (ROC) Curve: Practical Review for Radiologists , 2004, Korean journal of radiology.

[18]  Fei Gao,et al.  Deep Multimodal Distance Metric Learning Using Click Constraints for Image Ranking , 2017, IEEE Transactions on Cybernetics.

[19]  S. Grossberg,et al.  Texture segregation by visual cortex: Perceptual grouping, attention, and learning , 2007, Vision Research.

[20]  Xuelong Li,et al.  Geometric Mean for Subspace Selection , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Pietro Perona,et al.  Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[22]  Myo-Taeg Lim,et al.  Extended biologically inspired model for object recognition based on oriented Gaussian-Hermite moment , 2014, Neurocomputing.

[23]  Zhaohui Wu,et al.  Weakly Supervised Metric Learning for Traffic Sign Recognition in a LIDAR-Equipped Vehicle , 2016, IEEE Transactions on Intelligent Transportation Systems.

[24]  Dacheng Tao,et al.  Classification with Noisy Labels by Importance Reweighting , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Edmund T. Rolls,et al.  Invariant visual object recognition: biologically plausible approaches , 2015, Biological Cybernetics.

[26]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[27]  Xuelong Li,et al.  Enhanced Biologically Inspired Model for Object Recognition , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[28]  Changsheng Xu,et al.  User-Aware Image Tag Refinement via Ternary Semantic Analysis , 2012, IEEE Transactions on Multimedia.

[29]  Kechen Zhang,et al.  A Sparse Object Coding Scheme in Area V4 , 2011, Current Biology.

[30]  Rajat Raina,et al.  Efficient sparse coding algorithms , 2006, NIPS.

[31]  Peter Auer,et al.  Generic object recognition with boosting , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  C. Koch,et al.  Invariant visual representation by single neurons in the human brain , 2005, Nature.

[33]  Honglak Lee,et al.  Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.

[34]  Peijie Yin,et al.  A Novel Biologically Inspired Visual Cognition Model: Automatic Extraction of Semantics, Formation of Integrated Concepts, and Reselection Features for Ambiguity , 2016, IEEE Transactions on Cognitive and Developmental Systems.

[35]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[36]  Thomas Serre,et al.  Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  R. L. Valois,et al.  The orientation and direction selectivity of cells in macaque visual cortex , 1982, Vision Research.

[38]  Wen Gao,et al.  Manifold Alignment via Corresponding Projections , 2010, BMVC.

[39]  Tony Lindeberg,et al.  A computational theory of visual receptive fields , 2013, Biological Cybernetics.

[40]  T. Poggio,et al.  A model of V4 shape selectivity and invariance. , 2007, Journal of neurophysiology.

[41]  Seyed-Mahdi Khaligh-Razavi,et al.  How Can Selection of Biologically Inspired Features Improve the Performance of a Robust Object Recognition Model? , 2012, PloS one.

[42]  Ke Lu,et al.  $p$-Laplacian Regularized Sparse Coding for Human Activity Recognition , 2016, IEEE Transactions on Industrial Electronics.

[43]  Xiaolin Hu,et al.  Sparsity-Regularized HMAX for Visual Recognition , 2014, PloS one.

[44]  Dacheng Tao,et al.  DERF: Distinctive Efficient Robust Features From the Biological Modeling of the P Ganglion Cells , 2015, IEEE Transactions on Image Processing.

[45]  Min Tan,et al.  Robust object recognition via weakly supervised metric and template learning , 2016, Neurocomputing.

[46]  Stephen Grossberg,et al.  ARTSCENE: A neural system for natural scene classification. , 2009, Journal of vision.

[47]  Yuan Yan Tang,et al.  Sparse-based neural response for image classification , 2014, Neurocomputing.

[48]  Alice Caplier,et al.  Using Human Visual System modeling for bio-inspired low level image processing , 2010, Comput. Vis. Image Underst..

[49]  Peter Kovesi,et al.  Image Features from Phase Congruency , 1995 .

[50]  H. Sebastian Seung,et al.  The Manifold Ways of Perception , 2000, Science.

[51]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.