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

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.