title: Saliency detection using sparse and nonlinear feature representation.
abstract: An important aspect of visual saliency detection is how features that form an input image are represented. A popular theory supports sparse feature representation, an image being represented with a basis dictionary having sparse weighting coefficient. Another method uses a nonlinear combination of image features for representation. In our work, we combine the two methods and propose a scheme that takes advantage of both sparse and nonlinear feature representation. To this end, we use independent component analysis (ICA) and covariant matrices, respectively. To compute saliency, we use a biologically plausible center surround difference (CSD) mechanism. Our sparse features are adaptive in nature; the ICA basis function are learnt at every image representation, rather than being fixed. We show that Adaptive Sparse Features when used with a CSD mechanism yield better results compared to fixed sparse representations. We also show that covariant matrices consisting of nonlinear integration of color information alone are sufficient to efficiently estimate saliency from an image. The proposed dual representation scheme is then evaluated against human eye fixation prediction, response to psychological patterns, and salient object detection on well-known datasets. We conclude that having two forms of representation compliments one another and results in better saliency detection.
authors.affiliation: Beijing Key Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Shahzad
authors.identifier: 0000-0002-1023-9846
authors.initials: S
authors.lastname: Anwar
authors.affiliation: Beijing Key Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Qingjie
authors.initials: Q
authors.lastname: Zhao
authors.affiliation: School of Automation, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Muhammad Farhan
authors.initials: MF
authors.lastname: Manzoor
authors.affiliation: Centres of Excellence in Science and Applied Technologies, Islamabad 44000, Pakistan.
authors.forename: Saqib Ishaq
authors.initials: SI
authors.lastname: Khan
mesh_terms: D000465:Algorithms; D005133:Eye Movements / Q000502:physiology; D005403:Fixation, Ocular / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D008962:Models, Theoretical; D014796:Visual Perception / Q000502:physiology