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Title: Region-Based Saliency Detection and Its Application in Object Recognition
Authors: Ren, Zhixiang
Gao, Shenghua
Chia, Liang-Tien
Tsang, Ivor Wai-Hung
Keywords: Superpixel
Weighted sparse coding
Object recognition
Saliency detection
Saliency propagation
Issue Date: 2013
Source: Ren, Z., Gao, S., Chia, L-T., & Tsang, I. W.-H. (2014). Region-Based Saliency Detection and Its Application in Object Recognition. IEEE Transactions on Circuits and Systems for Video Technology, 24(5), 769-779.
Series/Report no.: IEEE Transactions on Circuits and Systems for Video Technology
Abstract: The objective of this paper is twofold. First, we introduce an effective region-based solution for saliency detection. Then, we apply the achieved saliency map to better encode the image features for solving object recognition task. To find the perceptually and semantically meaningful salient regions, we extract superpixels based on an adaptive mean shift algorithm as the basic elements for saliency detection. The saliency of each superpixel is measured by using its spatial compactness, which is calculated according to the results of Gaussian mixture model (GMM) clustering. To propagate saliency between similar clusters, we adopt a modified PageRank algorithm to refine the saliency map. Our method not only improves saliency detection through large salient region detection and noise tolerance in messy background, but also generates saliency maps with a well-defined object shape. Experimental results demonstrate the effectiveness of our method. Since the objects usually correspond to salient regions, and these regions usually play more important roles for object recognition than background, we apply our achieved saliency map for object recognition by incorporating a saliency map into sparse coding-based spatial pyramid matching (ScSPM) image representation. To learn a more discriminative codebook and better encode the features corresponding to the patches of the objects, we propose a weighted sparse coding for feature coding. Moreover, we also propose a saliency weighted max pooling to further emphasize the importance of those salient regions in feature pooling module. Experimental results on several datasets illustrate that our weighted ScSPM framework greatly outperforms ScSPM framework, and achieves excellent performance for object recognition.
ISSN: 1051-8215
DOI: 10.1109/TCSVT.2013.2280096
Schools: School of Computer Engineering 
Rights: © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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