Multimodal image matching via dual-codebook-based self-similarity hypercube feature descriptor and voting strategy
- Authors
- Wang, H.; Han, D. K.; Ko, H.
- Issue Date
- 9-10월-2014
- Publisher
- INST ENGINEERING TECHNOLOGY-IET
- Citation
- ELECTRONICS LETTERS, v.50, no.21, pp.1518 - 1519
- Indexed
- SCIE
SCOPUS
- Journal Title
- ELECTRONICS LETTERS
- Volume
- 50
- Number
- 21
- Start Page
- 1518
- End Page
- 1519
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/97105
- DOI
- 10.1049/el.2014.1802
- ISSN
- 0013-5194
- 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.
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