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- W2084000498 abstract "In this paper we propose a strategy to fuse visual features and unstructured-text data in a medical image retrieval system. The main goal of this work is to investigate whether the semantic information from text descriptions can be transfered to a visual similarity measure. Then, a system to search using the query-by-example paradigm is evaluated instead of a keyword-based search. We achieve this by using Latent Semantic Kernels to generate a new representation space whose coordinates define latent concepts that merge visual patterns and textual terms. The proposed method is tested in a medical image collection from the ImageCLEFmed08 challenge. The experimental evaluation tests the system using different image queries. The results show an improvement of the visual-text fused approach with respect to only using visual information." @default.
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- W2084000498 date "2010-03-29" @default.
- W2084000498 modified "2023-09-23" @default.
- W2084000498 title "Combining visual features and text data for medical image retrieval using latent semantic kernels" @default.
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- W2084000498 doi "https://doi.org/10.1145/1743384.1743442" @default.
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