A Review of Co-saliency Detection Technique: Fundamentals, Applications,
and Challenges
release_bcuoofzovjcztff67hqq5ppmlu
by
Dingwen Zhang, Huazhu Fu, Junwei Han, Ali Borji, Xuelong Li
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) |
1604.07090v3
access all versions, variants, and formats of this works (eg, pre-prints)