Approach for Object Recognition Based on a Computational Model of Feature Binding

This paper proposes a novel method for object recognition by using a computational model of feature binding, in which Gabor features are employed as the elementary features and correlation statistics provide the basis for implementing the feature binding. A group of object recognition experiments are conducted with this method, and the results prove the comparatively good performances with high recognition precision and high speed, indicating the validity of this method and the computational model.

[1]  A Treisman,et al.  Feature binding, attention and object perception. , 1998, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[2]  W. Singer,et al.  Temporal coding in the visual cortex: new vistas on integration in the nervous system , 1992, Trends in Neurosciences.

[3]  W. Singer,et al.  Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties , 1989, Nature.

[4]  Antonio R. Damasio,et al.  The Brain Binds Entities and Events by Multiregional Activation from Convergence Zones , 1989, Neural Computation.

[5]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

[6]  W Singer,et al.  Visual feature integration and the temporal correlation hypothesis. , 1995, Annual review of neuroscience.

[7]  J. Daugman.  Two-dimensional spectral analysis of cortical receptive field profiles , 1980, Vision Research.

[8]  Christoph von der Malsburg,et al.  The Correlation Theory of Brain Function , 1994 .

[9]  M. Usher,et al.  Segmentation, Binding, and Illusory Conjunctions , 1991, Neural Computation.

[10]  Joachim M. Buhmann,et al.  Distortion Invariant Object Recognition in the Dynamic Link Architecture , 1993, IEEE Trans. Computers.

[11]  Peter König,et al.  Binding by temporal structure in multiple feature domains of an oscillatory neuronal network , 1994, Biological Cybernetics.

[12]  David J. Kriegman,et al.  Acquiring linear subspaces for face recognition under variable lighting , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[14]  David J. Kriegman,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..