Region-Based Saliency Detection and Its Application in Object Recognition

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 region...

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Bibliographic Details
Published in:	IEEE transactions on circuits and systems for video technology 2014-05, Vol.24 (5), p.769-779
Main Authors:	Zhixiang Ren, Shenghua Gao, Liang-Tien Chia, Tsang, Ivor Wai-Hung
Format:	Article
Language:	eng
Subjects:	Algorithms Applied sciences Background noise Clustering algorithms Coding Coding, codes Detection, estimation, filtering, equalization, prediction Encoding Exact sciences and technology Feature extraction Image coding Image color analysis Image representation Information, signal and communications theory Matching Mathematical models Object recognition Pattern recognition Saliency Detection Saliency Propagation Search engines Signal and communications theory Signal processing Signal representation. Spectral analysis Signal, noise Superpixed Telecommunications and information theory Weighted Sparse Coding
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Summary:	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 1558-2205