Paper Info
Title
Co-Saliency Detection 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 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 co-saliency. Experiment results show that the proposed method can better detect the within-image co-saliency than existing algorithms.
Year
Venue
Field
2018
THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE
Computer vision,Salience (neuroscience),Computer science,Artificial intelligence,Machine learning
DocType
Citations 
PageRank 
Conference
1
0.34
References 
Authors
23
6
Name
Order
Citations
PageRank
Hongkai Yu15211.49
Kang Zheng2427.41
Jianwu Fang3174.11
Hao Guo4194.03
Wei Feng550161.25
Song Wang695479.55