Visual Representation in the Determination of Saliency

In this paper, we consider the role that visual representation plays in determining the behavior of a generic model of visual salience. There are a variety of different representations that have been employed to form an early visual representation of image structure. The experiments presented demonstrate that the choice of representation has an appreciable effect on the system behavior. The reasons for these differences are discussed, and generalized to implications for vision systems in general. In instances where a design choice is arbitrary, we look to the properties of visual representation in early visual processing in humans for answers. The results as a whole demonstrate the importance of filter choice and highlight some desirable properties of log-Gabor filters.

[1]  W. Lunscher,et al.  Optimal Edge Detector Design I: Parameter Selection and Noise Effects , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  John K. Tsotsos,et al.  Saliency Based on Information Maximization , 2005, NIPS.

[3]  William E. Higgins,et al.  Optimal Gabor-filter design for texture segmentation , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[4]  A. Parker,et al.  Two-dimensional spatial structure of receptive fields in monkey striate cortex. , 1988, Journal of the Optical Society of America. A, Optics and image science.

[5]  S. Nelson,et al.  An emergent model of orientation selectivity in cat visual cortical simple cells , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[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]  E Kaplan,et al.  Effects of dark adaptation on spatial and temporal properties of receptive fields in cat lateral geniculate nucleus. , 1979, The Journal of physiology.

[8]  John K. Tsotsos,et al.  Saliency, attention, and visual search: an information theoretic approach. , 2009, Journal of vision.

[9]  D. Chakrabarti,et al.  A fast fixed - point algorithm for independent component analysis , 1997 .

[10]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[11]  A. J. Bell,et al.  A Unifying Information-Theoretic Framework for Independent Component Analysis , 2000 .

[12]  D. Ferster,et al.  Prediction of Orientation Selectivity from Receptive Field Architecture in Simple Cells of Cat Visual Cortex , 2001, Neuron.

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

[14]  Jean-Franois Cardoso.  High-Order Contrasts for Independent Component Analysis , 1999, Neural Computation.

[15]  David A. Clausi,et al.  Designing Gabor filters for optimal texture separability , 2000, Pattern Recognit..