Paper Info
Title
A New Method and Benchmark for Detecting Co-Saliency Within a Single Image
Abstract
Recently, saliency detection in a single image and co-saliency detection in multiple images have drawn extensive research interest in the vision and multimedia communities. 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 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 benchmark dataset of 664 color images (two subsets) for within-image co-saliency detection. Experiment results show that the proposed method can better detect the within-image co-saliency than existing algorithms. The experimental results also show that the proposed method can be applied to detect the repetitive patterns in a single image and detect the co-saliency in multiple images.
Year
DOI
Venue
2020
10.1109/TMM.2020.2972165
IEEE Transactions on Multimedia
Keywords
DocType
Volume
Within-image co-saliency,convex optimization,low-rank based fusion
Journal
22
Issue
ISSN
Citations 
12
1520-9210
2
PageRank 
References 
Authors
0.36
0
5
Name
Order
Citations
PageRank
Hongkai Yu15211.49
Kang Zheng2427.41
jianwu fang3113.91
Hao Guo4194.03
Song Wang511912.91