Multi-modal Indexing and Retrieval Using an LSA-Based Kernel

This article proposes a Latent Semantic Analysis (LSA) based kernel function which effectively integrates low-level visual features with higher semantic ones into a common latent space that correlates multimodal features in the same latent space. The method’s potential was evaluated on two early fus...

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Bibliographic Details
Published inMultimodal Retrieval in the Medical Domain pp. 73 - 84
Main Authors Stathopoulos, Spyridon, Kalamboukis, Theodore
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing 2015
SeriesLecture Notes in Computer Science
Subjects
CBIR
Kernel LSA
LSA
LSI
Multi-modal retrieval
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Summary:This article proposes a Latent Semantic Analysis (LSA) based kernel function which effectively integrates low-level visual features with higher semantic ones into a common latent space that correlates multimodal features in the same latent space. The method’s potential was evaluated on two early fusion experiments in a realistic scenario of image retrieval as the one provided by the ImageCLEF medical task. The performance of the method depends on the distributions of the multimodal latent features and the experimental results show that it outperforms the latent indexing generated by singular value decomposition.
ISBN:3319244701
9783319244709
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-24471-6_7