Saliency detection based on global and local short-term sparse representation

Saliency detection has been considered to be an important issue in many computer vision tasks. In this paper, we propose a novel bottom-up saliency detection method based on sparse representation. Saliency detection includes two elements: image representation and saliency measurement. For an input image, first, the ICA algorithm is employed to learn a set of basis functions, then the image can be represented by this set of basis functions. Next, a global and local saliency framework is employed to measure the saliency. The global saliency is obtained through Low-Rank Representation (LRR), and the local saliency is obtained through a sparse coding scheme. The proposed method is compared with six state-of-the-art methods on two popular human eye fixation datasets, the experimental results indicate the accuracy of the proposed method to predict the human eye fixations.*Corresponding author.