Multimodal image matching via dualcodebook-based self-similarity hypercube feature descriptor and voting strategy

H. Wang, D. K. Han, Hanseok Ko

Research output: Contribution to journalArticle

Abstract

An effective feature descriptor is proposed for multimodal local-image patch matching. The conventional self-similarity hypercube (SSH) fails in multimodal image matching due to different intensities of multimodal images. To mitigate this problem, a dual-codebook clustering is proposed for generating the descriptors. It is based on extracting a codebook, respectively, from visible and thermal images but sharing the same k-means clustering index of the local features of visible and thermal image patches. The experimental results show that the proposed approach effectively solves the multimodal image quantisation problem. Moreover, a voting strategy based on the proposed similarity family function facilitates the multimodal image matching more robustly compared with the conventional state-of-the-art methods.

Original languageEnglish
Pages (from-to)1518-1520
Number of pages3
JournalElectronics Letters
Volume50
Issue number21
DOIs
Publication statusPublished - 2014 Jan 1

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Multimodal image matching via dualcodebook-based self-similarity hypercube feature descriptor and voting strategy'. Together they form a unique fingerprint.

  • Cite this