A Review of Co-saliency Detection Technique: Fundamentals, Applications, and Challenges release_bcuoofzovjcztff67hqq5ppmlu

by Dingwen Zhang, Huazhu Fu, Junwei Han, Ali Borji, Xuelong Li

Released as a article .

2017  

Abstract

Co-saliency detection is a newly emerging and rapidly growing research area in computer vision community. As a novel branch of visual saliency, co-saliency detection refers to the discovery of common and salient foregrounds from two or more relevant images, and can be widely used in many computer vision tasks. The existing co-saliency detection algorithms mainly consist of three components: extracting effective features to represent the image regions, exploring the informative cues or factors to characterize co-saliency, and designing effective computational frameworks to formulate co-saliency. Although numerous methods have been developed, the literature is still lacking a deep review and evaluation of co-saliency detection techniques. In this paper, we aim at providing a comprehensive review of the fundamentals, challenges, and applications of co-saliency detection. Specifically, we provide an overview of some related computer vision works, review the history of co-saliency detection, summarize and categorize the major algorithms in this research area, discuss some open issues in this area, present the potential applications of co-saliency detection, and finally point out some unsolved challenges and promising future works. We expect this review to be beneficial to both fresh and senior researchers in this field, and give insights to researchers in other related areas regarding the utility of co-saliency detection algorithms.
In text/plain format

Archived Files and Locations

application/pdf  3.4 MB
file_4bnkxpve75cizl65xhmkeh65ji
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2017-01-30
Version   v3
Language   en ?
arXiv  1604.07090v3
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: e31eebe3-8207-40dd-a110-e0d660004e1d
API URL: JSON