Dissertation/Thesis Abstract

Multi-Resolution Superpixels for Visual Saliency Detection in a Large Image Collection
by Singh, Anurag, Ph.D., University of Louisiana at Lafayette, 2015, 128; 3718565
Abstract (Summary)

Finding what attracts attention is an important task for visual processing. The visual saliency detection finds location of focus of visual attention on the most important or stand-out object in an image or a video sequence. These stand-out objects are composed of regions or superpixels. Moreover, the fixations occur in clusters, which are simulated using superpixels, where superpixels are clusters of pixels bound by the Gestalt principle for perceptual grouping. The visual saliency detection algorithms presented in the dissertation build on the premise that salient regions are high in color contrast, and when compared to other regions, they stand-out.

The most intuitive method to find the salient region is by comparing it to every other region. A region is ranked by its dissimilarities with respect to other regions and highlighting the statistically salient region proportional to their rank. Another way to compare regions is with respect to its local surrounding. Each region is represented with its Dominant Color Descriptor and the color difference between neighbors is found using the Earth Mover's Distance. The multi-resolution framework ensures robustness to the object size, location, and background type.

Image saliency detection using region contrast is often based on the premise that a salient region has a contrast with the background. But the natural biological method involves comparison to a large collection of similar regions. A novel method is presented to efficiently compare the image region to the regions derived from a large, stored collection of images. Intuitively finding video saliency is derived as a special case of a large collection with temporal reference. The various methods presented in the dissertation are tested on publicly available data sets and performs better than existing state-of-the-art methods.

Indexing (document details)
Advisor: Chu, Chee-Hung Henry
Commitee: Maida, Anthony S., Pratt, Michael A., Raghavan, Vijay V.
School: University of Louisiana at Lafayette
Department: Computer Science
School Location: United States -- Louisiana
Source: DAI-B 76/12(E), Dissertation Abstracts International
Subjects: Mass communications, Artificial intelligence, Computer science
Keywords: Early vision problems, Multi-resolution superpixels, Video saliency, Visual saliency detection
Publication Number: 3718565
ISBN: 978-1-321-98436-1
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