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Optical Engineering

Efficient classification using salient regions
Author(s): Bing Yang; Duanqing Xu
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Paper Abstract

Classification of images in many categorized datasets has rapidly improved in recent years. However, methods that perform well on particular datasets typically have one or more limitations, such as insufficient image-transformation invariance or significant performance degradation as the number of classes is increased. We attempt to overcome these challenges by extracting and matching visual features only at the focuses of visual saliency instead of the entire scene. First, we propose a visual-saliency detection method that combines the respective merits of color-saliency boosting and global-region-based contrast schemes to achieve more accurate saliency maps. Using a single feature type, we then obtain good performance on three public datasets when compared to other state-of-the-art approaches. Overall, our results prove that robust and efficient fixation-based classification, in terms of reducing the complexity of feature extraction, is possible.

Paper Details

Date Published: 6 July 2012
PDF: 8 pages
Opt. Eng. 51(7) 077201 doi: 10.1117/1.OE.51.7.077201
Published in: Optical Engineering Volume 51, Issue 7
Show Author Affiliations
Bing Yang, Zhejiang Univ. (China)
Duanqing Xu, Zhejiang Univ. (China)

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