Cross-Domain Co-Occurring Feature for Visible-Infrared Image Matching

As the two most commonly used imaging devices, infrared sensor, and visible sensor play a vital and essential role in the field of heterogeneous image matching. Therefore, visible-infrared image matching which aims to search images across them has important application and theoretical significance....

Full description

Bibliographic Details
Main Authors: Jing Li, Congcong Li, Tao Yang, Zhaoyang Lu
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8327803/
id doaj-80744b52e2bc40ca910f3d976fec78f8
record_format Article
spelling doaj-80744b52e2bc40ca910f3d976fec78f82021-03-29T21:01:00ZengIEEEIEEE Access2169-35362018-01-016176811769810.1109/ACCESS.2018.28206808327803Cross-Domain Co-Occurring Feature for Visible-Infrared Image MatchingJing Li0https://orcid.org/0000-0002-9043-8633Congcong Li1Tao Yang2Zhaoyang Lu3School of Telecommunications Engineering, Xidian University, Xi’an, ChinaSchool of Telecommunications Engineering, Xidian University, Xi’an, ChinaSchool of Computer Science, Northwestern Polytechnical University, Xi’an, ChinaSchool of Telecommunications Engineering, Xidian University, Xi’an, ChinaAs the two most commonly used imaging devices, infrared sensor, and visible sensor play a vital and essential role in the field of heterogeneous image matching. Therefore, visible-infrared image matching which aims to search images across them has important application and theoretical significance. However, due to the vastly different imaging principles, how to accurately match between visible and infrared image remains a challenge. In fact, the two images describe one scene from different aspects. There is a symbiotic relationship between their features, which we named as cross-domain co-occurring feature. In this paper, based on cross-domain co-occurring feature, we present a novel visible-infrared image matching algorithm. Concretely, co-occurring feature is first constructed by cross-domain image database and feature extraction approach. Then three visual vocabulary trees can be built by visible feature, infrared feature, and co-occurring feature. Thus, the symbiotic relationship between the two domains is established by cooccurring feature and vocabulary trees. With this relationship, each image is represented by a list of leaf node of co-occurring vocabulary tree. Finally, we measure the image similarity and the highest scoring image is the matching result. As a bi-directional method, we evaluate the proposed algorithm on two tasks: visible-toinfrared matching and infrared-to-visible matching. Experiments on the Korea Advanced Institute of Science and Technology all-day place recognition database captured from 42-km sequences demonstrate that cooccurring feature is effectiveness and efficiency to link different domains. And the matching approach also achieves superior performance.https://ieeexplore.ieee.org/document/8327803/Image matchingcross-domain co-occurring featurevisible-infrared image matching
collection DOAJ
language English
format Article
sources DOAJ
author Jing Li
Congcong Li
Tao Yang
Zhaoyang Lu
spellingShingle Jing Li
Congcong Li
Tao Yang
Zhaoyang Lu
Cross-Domain Co-Occurring Feature for Visible-Infrared Image Matching
IEEE Access
Image matching
cross-domain co-occurring feature
visible-infrared image matching
author_facet Jing Li
Congcong Li
Tao Yang
Zhaoyang Lu
author_sort Jing Li
title Cross-Domain Co-Occurring Feature for Visible-Infrared Image Matching
title_short Cross-Domain Co-Occurring Feature for Visible-Infrared Image Matching
title_full Cross-Domain Co-Occurring Feature for Visible-Infrared Image Matching
title_fullStr Cross-Domain Co-Occurring Feature for Visible-Infrared Image Matching
title_full_unstemmed Cross-Domain Co-Occurring Feature for Visible-Infrared Image Matching
title_sort cross-domain co-occurring feature for visible-infrared image matching
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2018-01-01
description As the two most commonly used imaging devices, infrared sensor, and visible sensor play a vital and essential role in the field of heterogeneous image matching. Therefore, visible-infrared image matching which aims to search images across them has important application and theoretical significance. However, due to the vastly different imaging principles, how to accurately match between visible and infrared image remains a challenge. In fact, the two images describe one scene from different aspects. There is a symbiotic relationship between their features, which we named as cross-domain co-occurring feature. In this paper, based on cross-domain co-occurring feature, we present a novel visible-infrared image matching algorithm. Concretely, co-occurring feature is first constructed by cross-domain image database and feature extraction approach. Then three visual vocabulary trees can be built by visible feature, infrared feature, and co-occurring feature. Thus, the symbiotic relationship between the two domains is established by cooccurring feature and vocabulary trees. With this relationship, each image is represented by a list of leaf node of co-occurring vocabulary tree. Finally, we measure the image similarity and the highest scoring image is the matching result. As a bi-directional method, we evaluate the proposed algorithm on two tasks: visible-toinfrared matching and infrared-to-visible matching. Experiments on the Korea Advanced Institute of Science and Technology all-day place recognition database captured from 42-km sequences demonstrate that cooccurring feature is effectiveness and efficiency to link different domains. And the matching approach also achieves superior performance.
topic Image matching
cross-domain co-occurring feature
visible-infrared image matching
url https://ieeexplore.ieee.org/document/8327803/
work_keys_str_mv AT jingli crossdomaincooccurringfeatureforvisibleinfraredimagematching
AT congcongli crossdomaincooccurringfeatureforvisibleinfraredimagematching
AT taoyang crossdomaincooccurringfeatureforvisibleinfraredimagematching
AT zhaoyanglu crossdomaincooccurringfeatureforvisibleinfraredimagematching
_version_ 1724193742353596416