NDLI: Visual Saliency Detection Using Group Lasso Regularization in Videos of Natural Scenes

Content Provider	Springer Nature Link
Author	Souly, Nasim Shah, Mubarak
Copyright Year	2015
Abstract	Visual saliency is the ability of a vision system to promptly select the most relevant data in the scene and reduce the amount of visual data that needs to be processed. Thus, its applications for complex tasks such as object detection, object recognition and video compression have attained interest in computer vision studies. In this paper, we introduce a novel unsupervised method for detecting visual saliency in videos of natural scenes. For this, we divide a video into non-overlapping cuboids and create a matrix whose columns correspond to intensity values of these cuboids. Simultaneously, we segment the video using a hierarchical segmentation method and obtain super-voxels. A dictionary learned from the feature data matrix of the video is subsequently used to represent the video as coefficients of atoms. Then, these coefficients are decomposed into salient and non-salient parts. We propose to use group lasso regularization to find the sparse representation of a video, which benefits from grouping information provided by super-voxels and extracted features from the cuboids. We find saliency regions by decomposing the feature matrix of a video into low-rank and sparse matrices by using robust principal component analysis matrix recovery method. The applicability of our method is tested on four video data sets of natural scenes. Our experiments provide promising results in terms of predicting eye movement using standard evaluation methods. In addition, we show our video saliency can be used to improve the performance of human action recognition on a standard dataset.
Starting Page	93
Ending Page	110
Page Count	18
File Format	PDF
ISSN	09205691
Journal	International Journal of Computer Vision
Volume Number	117
Issue Number	1
e-ISSN	15731405
Language	English
Publisher	Springer US
Publisher Date	2015-08-15
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Visual saliency Sparse coding Super-voxels Group lasso Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence (incl. Robotics) Image Processing and Computer Vision Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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