NDLI: Regularized Feature Reconstruction for Spatio-Temporal Saliency Detection

Content Provider	IEEE Xplore Digital Library
Author	Zhixiang Ren Shenghua Gao Liang-Tien Chia Rajan, D.
Copyright Year	1992
Abstract	Multimedia applications such as image or video retrieval, copy detection, and so forth can benefit from saliency detection, which is essentially a method to identify areas in images and videos that capture the attention of the human visual system. In this paper, we propose a new spatio-temporal saliency detection framework on the basis of regularized feature reconstruction. Specifically, for video saliency detection, both the temporal and spatial saliency detection are considered. For temporal saliency, we model the movement of the target patch as a reconstruction process using the patches in neighboring frames. A Laplacian smoothing term is introduced to model the coherent motion trajectories. With psychological findings that abrupt stimulus could cause a rapid and involuntary deployment of attention, our temporal model combines the reconstruction error, regularizer, and local trajectory contrast to measure the temporal saliency. For spatial saliency, a similar sparse reconstruction process is adopted to capture the regions with high center-surround contrast. Finally, the temporal saliency and spatial saliency are combined together to favor salient regions with high confidence for video saliency detection. We also apply the spatial saliency part of the spatio-temporal model to image saliency detection. Experimental results on a human fixation video dataset and an image saliency detection dataset show that our method achieves the best performance over several state-of-the-art approaches.
Sponsorship	IEEE Signal Processing Society
Page Count	13
File Size	1895148
Starting Page	3120
Ending Page	3132
File Format	PDF
ISSN	10577149
Volume Number	22
Issue Number	8
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	motion trajectory Spatio-temporal saliency detection feature reconstruction
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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