MULTIMODAL IMAGE CLASSIFICATION USING INVERTED LOCAL PATTERNS
Rafi Md Najmus Sadat, Md. Abdul Mottalib, Sheikh Faridul Hasan, Md. Musfequs SalehinAbstract
Multimodality during imaging suffers from significant contrast variation between the images of the same scene. Due to this large variation, existing image classification and retrieval algorithms are not performing well for multimodal images. So, to solve this problem of multimodality, we have proposed a Local Binary Pattern (LBP) based modality invariant descriptor. The quantitative results show that the proposed descriptor outperforms not only other state of the art modality invariant descriptors but also famous LBP variants in terms of classification accuracy.
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