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A feature descriptor based on the local patch clustering distribution for illumination-robust image matching

Authors
Wang, HanYoon, Sang MinHan, David K.Ko, Hanseok
Issue Date
15-7월-2017
Publisher
ELSEVIER
Keywords
Local patch clustering distribution; Feature descriptor illumination change; Image matching
Citation
PATTERN RECOGNITION LETTERS, v.94, pp.46 - 54
Indexed
SCIE
SCOPUS
Journal Title
PATTERN RECOGNITION LETTERS
Volume
94
Start Page
46
End Page
54
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/82827
DOI
10.1016/j.patrec.2017.05.010
ISSN
0167-8655
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.
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