NDLI: Capturing image structure with probabilistic index maps

Content Provider	IEEE Xplore Digital Library
Author	Jojic, N. Caspi, Y.
Copyright Year	2004
Abstract	One of the major problems in modeling images for vision tasks is that images with very similar structure may locally have completely different appearance, e.g., images taken under different illumination conditions, or the images of pedestrians with different clothing. While there have been many successful attempts to address these problems in application-specific settings, we believe that underlying a large set of problems in vision is a representational deficiency of intensity-derived local measurements that are the basis of most efficient models. We argue that interesting structure in images is better captured when the image is defined as a matrix whose entries are discrete indices to a separate palette of possible intensities, colors or other features, much like the image representation often used to save on storage. In order to model the variability in images, we define an image class not by a single index map, but by a probability distribution over the index maps, which can be automatically estimated from the data, and which we call probabilistic index maps. The existing algorithms can be adapted to work with this representation, as we illustrate in this paper on the example of transformation-invariant clustering and background subtraction. Furthermore, the probabilistic index map representation leads to algorithms with computational costs proportional to either the size of the palette or the log of the size of the palette, making the cost of significantly increased invariance to non-structural changes quite bearable.
Sponsorship	IEEE Comput. Soc
File Size	742174
File Format	PDF
ISBN	0769521584
ISSN	10636919
DOI	10.1109/CVPR.2004.1315034
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image storage Clustering algorithms Color Lighting Image representation Probability distribution Costs Pixel Clothing Computational efficiency
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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