Multimodal image matching via dual‐codebook‐based self‐similarity hypercube feature descriptor and voting strategy. Issue 21 (1st October 2014) - Wang, H.; Han, D.K.; Ko, H.
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.