Pre-attentive visual segmentation algorithm for cognitive robots

Pre-attentive segmentation is one of important process for object-based visual perception of cognitive robots. This paper proposes a Bhattacharyya distance based irregular pyramid method for pre-attentive segmentation. This algorithm hierarchically builds each level of the irregular pyramid by accumulating similar and spatially close nodes at the level below, with the result that the final segments emerge in this process as they are represented by single nodes at certain levels. The Bhattacharyya distance is used to estimate the intralevel similarity so as to improve the segmentation accuracy and robustness to noise. The ability to self-determine the number of segments is also developed for the pyramidal accumulation process by proposing an adaptive neighbor search method. Experimental results have shown that the proposed algorithm outperforms existing image segmentation algorithms in terms of segmentation accuracy and robustness to noise.

[1]  J. Wolfe,et al.  Preattentive Object Files: Shapeless Bundles of Basic Features , 1997, Vision Research.

[2]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[3]  Donald Geman,et al.  Boundary Detection by Constrained Optimization , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[5]  Olga Veksler,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Edgar Arce Santana,et al.  Hidden Markov Measure Field Models for Image Segmentation , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  George K. I. Mann,et al.  An Object-Based Visual Attention Model for Robotic Applications , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[9]  Jitendra Malik,et al.  Contour and Texture Analysis for Image Segmentation , 2001, International Journal of Computer Vision.

[10]  J. Duncan.  Selective attention and the organization of visual information. , 1984, Journal of experimental psychology. General.

[11]  Carl R Olson,et al.  Object-based vision and attention in primates , 2001, Current Opinion in Neurobiology.

[12]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Ronen Basri,et al.  Segmentation and boundary detection using multiscale intensity measurements , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[14]  Leo Grady,et al.  Isoperimetric graph partitioning for image segmentation , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  George K. I. Mann,et al.  A Goal-Directed Visual Perception System Using Object-Based Top–Down Attention , 2012, IEEE Transactions on Autonomous Mental Development.

[16]  H. Nothdurft.  The role of features in preattentive vision: Comparison of orientation, motion and color cues , 1993, Vision Research.

[17]  Azriel Rosenfeld,et al.  Hierarchical Image Analysis Using Irregular Tessellations , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  B. Scholl.  Objects and attention: the state of the art , 2001, Cognition.

[19]  Zhuowen Tu,et al.  Image Segmentation by Data-Driven Markov Chain Monte Carlo , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Leo Grady,et al.  Random Walks for Image Segmentation , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Ronen Basri,et al.  Hierarchy and adaptivity in segmenting visual scenes , 2006, Nature.

[22]  Jean-Michel Jolion.  Stochastic pyramid revisited , 2003, Pattern Recognit. Lett..

[23]  Peter Meer,et al.  Stochastic image pyramids , 1989, Comput. Vis. Graph. Image Process..

[24]  Dina E. Melas,et al.  Double Markov random fields and Bayesian image segmentation , 2002, IEEE Trans. Signal Process..

[25]  Donald Geman,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .

[26]  Ronen Basri,et al.  Fast multiscale image segmentation , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).