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A Common Set of Perceptual Observables for Grouping, Figure-Ground Discrimination, and Texture Classification
April 2003 (vol. 25 no. 4)
pp. 458-474

Abstract—We present a complete set of perceptual observables that provides a unified image description for grouping, figure-ground separation, and texture analysis. Although much progess has been made recently in treating contours and texture simultaneously for image segmentation and grouping, current approaches rely on different models for contours, regions, and texture such as one-dimensional intensity discontinuities for contours and filter bank responses for texture. This results in expensive computation that arbitrates between these disparate representations at each pixel. In our approach, salient image content such as contours, regions, and texture are represented in a common, low-level framework of image observables. We model the image as a partition of surfaces bounded by intensity discontinuities and derive perceptual measures as relations between neighboring surfaces. This enables us to extend the traditional Gestalt measures based on local edge geometry and contrast to region-based measures that jointly exploit large-scale image topology, photometry, and geometry. These measures provide a natural basis for grouping on multidimensional similarity criteria and texture is directly derived as relational properties on local region neighborhoods. The viability of our model is demonstrated by applying the common observables to texture recognition, figure-ground separation, and generic image segmentation. The texture classification algorithm approaches or exceeds the accuracy of filter bank approaches on both periodic and nonperiodic textures that have significant 3D structure. The measures are invariant to image rotation and slowly varying against large changes in illumination, viewpoint, and scale. The same perceptual measures are successfully applied in a difficult figure-ground separation problem in aerial images. Regions are first filtered, then grouped, using an efficient search algorithm based on perceptual salience to delineate objects of interest. Results for both are shown on large sets of complex, real-world images exhibiting difficult conditions.

[1] A. Amir and M. Lindenbaum, “A Generic Grouping Algorithm and Its Quantitative Analysis,” IEEE Tran. Pattern Analysis and Machine Intelligence, vol. 20, no. 2, pp. 168-185, Feb. 1998.
[2] R. Basri and D. Jacobs, “Projective Alignment with Regions,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 5, pp. 519-527, May 2001.
[3] J. Canny, “A Computational Approach to Edge Detection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679-698, June 1986.
[4] D. Comaniciu and P. Meer, Mean Shift: A Robust Approach towards Feature Space A Analysis IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603-619, May 2002.
[5] O. Cula and K. Dana, “Compact Representations for Bi-Directional Texture Functions,” Proc. Conf. Computer Vision and Pattern Recognition, Dec. 2001.
[6] K.J. Dana and S.K. Nayar, “Histogram Model for 3D Textures,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 618-624, 1998.
[7] K.J. Dana, B. van Ginneken, S.K. Nayar, and J.J. Koenderink, “Reflectance and Texture of Real-World Surfaces,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 151-157, 1997.
[8] K.J. Dana, B. van Ginneken, S.K. Nayar, and J.J. Koenderink, “Reflectance and Texture of Real-World Surfaces,” ACM Trans. Graphics, vol. 18, no. 1, pp. 1-34, Jan. 1999.
[9] Y. Deng and B. S. Manjunath, Unsupervised Segmentation of Color-Texture Regions in Images and Video IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 8, pp. 800-810, Aug. 2001.
[10] R. Duda, P. Hart, and D. Stork, Pattern Classification. New York: John Wiley&Sons, 2001.
[11] G. Guy and G. Medioni, “Inferring Global Perceptual Contours from Local Features,” Int'l J. Computer Vision, vol. 20, pp. 113-133, 1996.
[12] L. Herault and R. Horaud, “Figure Ground Discrimination: A Combinatorial Optimization Method,” IEEE Tran. Pattern Analysis and Machine Intelligence, vol. 15, no. 9, pp. 899-914, Sept. 1993.
[13] A. Hoogs and J. Mundy, “An Integrated Boundary and Region Approach to Perceptual Grouping,” Proc. Int'l Conf. Pattern Recognition, vol. 1, pp. 284-290, Sept. 2000.
[14] T. Hofmann, J. Puzicha, and J.M. Buhmann, “Unsupervised Texture Segmentation in a Deterministic Annealing Framework,” IEEE Trans. Pattern Analysis and Machince Intelligence, vol. 20, pp. 803-818, 1998.
[15] D.W. Jacobs, "Robust and Efficient Detection of Salient Convex Groups," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, pp. 23-37, Jan. 1996.
[16] S. Konishi and A. Yuille, “Stastistical Cues for Domain Specific Image Segmentation with Performance Analysis,” Proc. Conf. Computer Vision and Pattern Recognition, June 2000.
[17] A. Leonardis,A. Gupta,, and R. Bajcsy,“Segmentation as the search for the best description of the image in terms of primitives,” Proc. Int’l Conf. Computer Vision, pp. 121-125, 1990.
[18] T. Leung and J. Malik, “Recognizing Surfaces Using Three-Dimensional Textons,” Proc. Seventh Int'l Conf. Computer Vision, 1999.
[19] J. Malik, S. Belongie, J. Shi, and T. Leung, “Textons, Contours and Regions: Cue Combination in Image Segmentation,” Proc. Int'l Conf. Computer Vision, 1999.
[20] B.S. Manjunath and W.Y. Ma, “Texture Features for Browsing and Retrieval of Image Data,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp. 837-842, Aug. 1996
[21] R. Nock, “Fast and Reliable Color Region Merging Inspired by Decision Tree Pruning,” Proc. Conf. Computer Vision and Pattern Recognition, pp. 271-276, Dec. 2001.
[22] T. Randen and J.H. Husoy, “Filtering for Texture Classification: A Comparative Study,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 4, pp. 291-310, Apr. 1999.
[23] I. Rock and S. Palmer, “The Legacy of Gestalt Psychology,” Scientific Am., pp. 84-90, Dec. 1990.
[24] S. Sarkar and K.L. Boyer, “Quantitative Measures of Change Based on Feature Organization: Eigenvalues and Eigenvectors,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, pp. 478-483, June 1996.
[25] S. Sarkar and P. Soundararajan, Supervised Learning of Large Perceptual Organization: Graph Spectral Partitioning and Learning Automata IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 5, pp. 504-525, May 2000.
[26] J. Shi and J. Malik, “Normalized Cuts and Image Segmenation,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, pp. 731-737, June 1997.
[27] J. Shi and J. Malik, Normalized Cuts and Image Segmentation IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888-905, Aug. 2000.
[28] P. Suen and G. Healey, “The Analysis and Recognition of Real-World Textures in Three Dimensions,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 5, pp. 491-503, May 2000.
[29] Z. Wu and R. Leahy, “An Optimal Graph Theoretic Approach to Data Clustering: Theory and Its Application to Image Segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1,101-1,113, Nov. 1993.
[30] A. Zalesny and L. Van Gool, “Multiview Texture Models,” Proc. Conf. Computer Vision and Pattern Recognition, Dec. 2001.
[31] S.C. Zhu and A. Yuille, “Region Competition: Unifying Snakes, Region Growing and Bayes/MDL for Multiband Image Segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, pp. 884-900, 1996.
[32] Z. Tu, S. Zhu, and H. Shum, Image Segmentation by Data Driven Markov Chain Monte Carlo Proc. Int'l Conf. Computer Vision, vol. 2, pp. 131-138 July 2001.

Index Terms:
Perceptual grouping, texture classification, image segmentation, figure-ground separation.
Citation:
Anthony Hoogs, Roderic Collins, Robert Kaucic, Joseph Mundy, "A Common Set of Perceptual Observables for Grouping, Figure-Ground Discrimination, and Texture Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 4, pp. 458-474, April 2003, doi:10.1109/TPAMI.2003.1190572
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