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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Co-Saliency Detection Within a Single Image

March 15, 2023

Authors

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


Published:

2018-02-08

Proceedings:

Proceedings of the AAAI Conference on Artificial Intelligence, 32

Volume

Issue:

Thirty-Second AAAI Conference on Artificial Intelligence 2018

Track:

AAAI Technical Track: Vision

Downloads:

Download PDF

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 cosaliency. Experiment results show that the proposed method can better detect the within-image co-saliency than existing algorithms.

DOI:

10.1609/aaai.v32i1.12310


AAAI

Thirty-Second AAAI Conference on Artificial Intelligence 2018


ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)


Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Topics: AAAI

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