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

Han Wang, Sang Min Yoon, David K. Han, Hanseok Ko

    Research output: Contribution to journalArticlepeer-review

    12 Citations (Scopus)

    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.

    Original languageEnglish
    Pages (from-to)46-54
    Number of pages9
    JournalPattern Recognition Letters
    Volume94
    DOIs
    Publication statusPublished - 2017 Jul 15

    Bibliographical note

    Funding Information:
    This work was supported by Korea University and Nantong University (Grant no.GY12016020).

    Publisher Copyright:
    © 2017 Elsevier B.V.

    Keywords

    • Feature descriptor illumination change
    • Image matching
    • Local patch clustering distribution

    ASJC Scopus subject areas

    • Software
    • Signal Processing
    • Computer Vision and Pattern Recognition
    • Artificial Intelligence

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