Abstract: With the rapid development of image acquisition technology,the original digital images are increasing and becoming more and more clear.When processing these images,the existing co-saliency detection methods need enormous computer memory along with high computational complexity.These limitations make it hard to satisfy the demand of real-time user interaction.This paper proposed a fast co-saliency detection method based on the image block method and sparse principal feature extraction method.Firstly,the image is averagely divided into several uniform blocks,and the low-level features are extracted from Lab and RGB color spaces.Then truncated power and parse principal components method are proposed to extract sparse principal features,which can remain the characteristics of the original image to the maximum extent and reduce the number of feature points and attributes.Furthermore,K-Means method is adopted to cluster the extracted sparse principal features,and calculate the three salient feature weights.Finally,the saliency map of the single image and that of multi images which are generated by feature fusion are combined to generate co-saliency map.The proposed method was tested and simulated on two benchmark datasets:Co-saliency Pairs and CMU Cornell iCoseg datasets.And the experimental results demonstrate that BSFCoS has better effectiveness and efficiency on multiple images compared with the existing co-saliency methods.

Key words: Saliency detection,Fast,Co-saliency,Truncated power,K-Means