Saliency fusion via sparse and double low rank decomposition

Abstract

Video surveillance-oriented biometrics is a very challenging task and has tremendous significance to the security of public places. Saliency detection can support video surveillance systems by reducing redundant information and highlighting the critical regions, e.g., faces. Existing saliency detection models usually behave differently over an individual image, and meanwhile these methods often complement each other. This paper addresses the problem of fusing various saliency detection methods such that the fusion result outperforms each of the individual methods. A novel sparse and double low rank decomposition model (SDLRD) is proposed for such a purpose. Given an image described by multiple saliency maps, SDLRD uses a unified low rank assumption to characterize the object regions and background regions respectively. Furthermore, SDLRD depicts the noises covered on the whole image by a sparse matrix, based on the observation that the noises generally lie in a sparse subspace. After reducing the influence by noises, the correlations among object and background regions can be enhanced simultaneously. In this way, an image is represented as the combination of a sparse matrix plus two low rank matrices. As such, we cast the saliency fusion as a subspace decomposition problem and aim at inferring the low rank one that indicates the salient target. Experiments on five datasets demonstrate that our fusion method consistently outperforms each individual saliency method and other state-of-the-art saliency fusion approaches. Specifically, the proposed method is demonstrated to be effective on the applications of video-based biometrics such as face detection.