Learning a Deep Representative Saliency Map With Sparse Tensors

The past few years have witnessed the prosperity of establishing deep architectures for modeling complex structured data. In this paper, motivated by the hierarchical, multi-scale and sparse characteristics of Human Visual System (HVS), we advance a new Deep and Sparse Representative (DSR) saliency detection approach for natural images. Under the assumption that salient regions are considered as sparse representatives of an image, we gradually select the salient regions via a deep and hierarchical sparse coding model. Moreover, in order to well preserve image structures and achieve more efficient coding, we formulate image patches as tensors and propose a Sparse Tensor Coding based DSR (STC-DSR) approach to find representatives. A generalized Laplacian regularizer is further cast on the coding to refine the coefficients. Finally, the saliency map is formulated by a spatial aggregation and multi-scale fusion of multilayer representatives. The proposed STC-DSR is investigated on the synthetic and public datasets and the experimental results prove its efficiency and superiority to its counterparts.