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.
|Advisor:||Chu, Chee-Hung Henry|
|Commitee:||Maida, Anthony S., Pratt, Michael A., Raghavan, Vijay V.|
|School:||University of Louisiana at Lafayette|
|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|
Copyright in each Dissertation and Thesis is retained by the author. All Rights Reserved
The supplemental file or files you are about to download were provided to ProQuest by the author as part of a
dissertation or thesis. The supplemental files are provided "AS IS" without warranty. ProQuest is not responsible for the
content, format or impact on the supplemental file(s) on our system. in some cases, the file type may be unknown or
may be a .exe file. We recommend caution as you open such files.
Copyright of the original materials contained in the supplemental file is retained by the author and your access to the
supplemental files is subject to the ProQuest Terms and Conditions of use.
Depending on the size of the file(s) you are downloading, the system may take some time to download them. Please be