Co-Saliency Detection Within a Single Image


  • Hongkai Yu University of South Carolina
  • Kang Zheng University of South Carolina
  • Jianwu Fang Xi'an Jiaotong University; Chang'an University
  • Hao Guo University of South Carolina
  • Wei Feng Tianjin University
  • Song Wang Tianjin University; University of South Carolina


Co-Saliency, optimization, low rank


Recently, saliency detection in a single image and co-saliency detection in multiple images have drawn extensive research interest in the vision community. In this paper, we investigate a new problem of co-saliency detection within a single image, i.e., detecting within-image co-saliency. By identifying common saliency within an image, e.g., highlighting multiple occurrences of an object class with similar appearance, this work can benefit many important applications, such as the detection of objects of interest, more robust object recognition, reduction of information redundancy, and animation synthesis. We propose a new bottom-up method to address this problem. Specifically, a large number of object proposals are first detected from the image. Then we develop an optimization algorithm to derive a set of proposal groups, each of which contains multiple proposals showing good common saliency in the original image. For each proposal group, we calculate a co-saliency map and then use a low-rank based algorithm to fuse the maps calculated from all the proposal groups for the final co-saliency map in the image. In the experiment, we collect a new dataset of 364 color images with within-image cosaliency. Experiment results show that the proposed method can better detect the within-image co-saliency than existing algorithms.




How to Cite

Yu, H., Zheng, K., Fang, J., Guo, H., Feng, W., & Wang, S. (2018). Co-Saliency Detection Within a Single Image. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from