A feature descriptor based on the local patch clustering distribution for illumination-robust image matching
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wang, Han | - |
dc.contributor.author | Yoon, Sang Min | - |
dc.contributor.author | Han, David K. | - |
dc.contributor.author | Ko, Hanseok | - |
dc.date.accessioned | 2021-09-03T03:58:34Z | - |
dc.date.available | 2021-09-03T03:58:34Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2017-07-15 | - |
dc.identifier.issn | 0167-8655 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/82827 | - |
dc.description.abstract | This paper proposes a feature descriptor based on the local patch clustering distribution (LPCD), which preserves the salient features of a given image following changes in illumination. To mitigate the effects of illumination change, the proposed LPCD methodology consists of two steps. First, a local patch clustering assignment map is constructed by pairing the source image with a reference image. To resolve the quantization problem caused by an illumination change, a dual-codebook clustering method is employed so that an effective local patch clustering feature space can be constructed. Second, in the feature encoding process, the impact of the informative local patches that contain textural information is enhanced when using a saliency detection response as a method of weighting every local patch when the histogram feature is extracted. Experimental results show that the proposed local patch clustering space is more robust than the conventional intensity order-based space in response to changes in illumination. (C) 2017 Elsevier B.V. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER | - |
dc.subject | SCALE | - |
dc.title | A feature descriptor based on the local patch clustering distribution for illumination-robust image matching | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Ko, Hanseok | - |
dc.identifier.doi | 10.1016/j.patrec.2017.05.010 | - |
dc.identifier.scopusid | 2-s2.0-85019749898 | - |
dc.identifier.wosid | 000404696700007 | - |
dc.identifier.bibliographicCitation | PATTERN RECOGNITION LETTERS, v.94, pp.46 - 54 | - |
dc.relation.isPartOf | PATTERN RECOGNITION LETTERS | - |
dc.citation.title | PATTERN RECOGNITION LETTERS | - |
dc.citation.volume | 94 | - |
dc.citation.startPage | 46 | - |
dc.citation.endPage | 54 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.subject.keywordPlus | SCALE | - |
dc.subject.keywordAuthor | Local patch clustering distribution | - |
dc.subject.keywordAuthor | Feature descriptor illumination change | - |
dc.subject.keywordAuthor | Image matching | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.