J Biomed Inform - Histology image search using multimodal fusion.

Tópicos

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Resumo

This work proposes a histology image indexing strategy based on multimodal representations obtained from the combination of visual features and associated semantic annotations. Both data modalities are complementary information sources for an image retrieval system, since visual features lack explicit semantic information and semantic terms do not usually describe the visual appearance of images. The paper proposes a novel strategy to build a fused image representation using matrix factorization algorithms and data reconstruction principles to generate a set of multimodal features. The methodology can seamlessly recover the multimodal representation of images without semantic annotations, allowing us to index new images using visual features only, and also accepting single example images as queries. Experimental evaluations on three different histology image data sets show that our strategy is a simple, yet effective approach to building multimodal representations for histology image search, and outperforms the response of the popular late fusion approach to combine information.

Resumo Limpo

work propos histolog imag index strategi base multimod represent obtain combin visual featur associ semant annot data modal complementari inform sourc imag retriev system sinc visual featur lack explicit semant inform semant term usual describ visual appear imag paper propos novel strategi build fuse imag represent use matrix factor algorithm data reconstruct principl generat set multimod featur methodolog can seamless recov multimod represent imag without semant annot allow us index new imag use visual featur also accept singl exampl imag queri experiment evalu three differ histolog imag data set show strategi simpl yet effect approach build multimod represent histolog imag search outperform respons popular late fusion approach combin inform

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