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- W2977737095 abstract "In this paper, we propose an approach to negotiate the gap between low-level image features and the human interpretation of the image. Taking the cue from text-based retrieval techniques, we construct visual keywords using vector quantization of small- sized image tiles. Both visual and textual keywords are combined and used to represent an image as a single multimodal vector. We use a diffusion kernel-based non-linear approach to fuse the visual and textual keywords. By comparing the performance of this approach with a low-level features-based approach, we demonstrate that visual keywords, when combined with textual keywords, improve the image retrieval results significantly." @default.
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- W2977737095 date "2007-12-01" @default.
- W2977737095 modified "2023-09-25" @default.
- W2977737095 title "Application of Diffusion Kernel in Multimodal Image Retrieval" @default.
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- W2977737095 doi "https://doi.org/10.1109/ism.workshops.2007.53" @default.
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