Texture orientation for sorting photos "at a glance"

Investigates a measure of "dominant perceived orientation" that has been developed to match the output of a human study involving 40 subjects. The results of this measure are compared with humans analyzing seven "teaser" images to test its effectiveness for finding perceptually dominant orientations. The use of low-level orientation is then applied to a "quick search" problem important in image database applications. Since both pigeons and humans are able to perform coarse classification of certain kinds of scenes, e.g., city from country, without taking time or brain-power to solve the image understanding problem, the authors conjecture that the collective behavior of low-level textural features such as orientation may be doing most of the work. The authors demonstrate a simple test of global multiscale orientation for quickly searching a database of vacation photos for likely "city/suburb" shots. The orientation features achieve agreement with human classification in 91 out of 98 of the scenes.

[1]  Phil Brodatz,et al.  Textures: A Photographic Album for Artists and Designers , 1966 .

[2]  D. Hubel,et al.  Receptive fields and functional architecture of monkey striate cortex , 1968, The Journal of physiology.

[3]  Ruzena Bajcsy,et al.  Computer Description of Textured Surfaces , 1973, IJCAI.

[4]  R. Herrnstein,et al.  Natural concepts in pigeons. , 1976, Journal of experimental psychology. Animal behavior processes.

[5]  K. Fieandt,et al.  The perceptual world , 1977 .

[6]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[7]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[8]  Andrew P. Witkin,et al.  Analyzing Oriented Patterns , 1985, IJCAI.

[9]  S. Chaudhuri,et al.  A Fourier Domain Directional Filterng Method for Analysis of Collagen Alignment in Ligaments , 1987, IEEE Transactions on Biomedical Engineering.

[10]  J. Bigun,et al.  Optimal Orientation Detection of Linear Symmetry , 1987, ICCV 1987.

[11]  Anil K. Jain,et al.  Unsupervised texture segmentation using Gabor filters , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[12]  J. Bergen,et al.  Computational Modeling of Visual Texture Segregation , 1991 .

[13]  A. Ravishankar Rao,et al.  Computing oriented texture fields , 1991, CVGIP Graph. Model. Image Process..

[14]  Edward H. Adelson,et al.  The Design and Use of Steerable Filters , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Anil K. Jain,et al.  Unsupervised texture segmentation using Gabor filters , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[16]  W. Freeman Steerable filters and local analysis of image structure , 1992 .

[17]  Rosalind W. Picard,et al.  Finding similar patterns in large image databases , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[18]  Bidyut Baran Chaudhuri,et al.  Detection and gradation of oriented texture , 1993, Pattern Recognit. Lett..

[19]  Rosalind W. Picard,et al.  Finding perceptually dominant orientations in natural textures. , 1994, Spatial vision.