Saliency detection based on graph and independent component analysis with reference

As a preprocessing step of many applications, such as object recognition, image retrieval and scene analysis, saliency detection plays an important role and remains a challenging and significant problem in computer vision. Most existing bottom-up methods utilize local or global contrast information to compute the saliency maps, whereas a few methods generate saliency maps with the use of background cues. This work presents a saliency detection method by applying independent component analysis with reference (ICA-R) algorithm to the background cues, which improves the performance of the final saliency maps. First, we segment the input image into superpixels. Second, we take superpixels on each side of image as reference signals to do ICA-R learning, respectively. Then, four saliency maps generated from the learning algorithm are integrated into one background saliency map. Finally, a graph-based manifold ranking algorithm is done to generate the final saliency maps. By doing experiments on a large publicly available database, we demonstrate that the proposed ICA-R saliency detection algorithm performs better than the state-of-the-art methods.