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CIVR
2010
Springer

Multi modal semantic indexing for image retrieval

9 years 3 months ago
Multi modal semantic indexing for image retrieval
Popular image retrieval schemes generally rely only on a single mode, (either low level visual features or embedded text) for searching in multimedia databases. Many popular image collections (eg. those emerging over Internet) have associated tags, often for human consumption. A natural extension is to combine information from multiple modes for enhancing effectiveness in retrieval. In this paper, we propose two techniques: Multi-modal Latent Semantic Indexing (MMLSI) and Multi-Modal Probabilistic Latent Semantic Analysis (MMpLSA). These methods are obtained by directly extending their traditional single mode counter parts. Both these methods incorporate visual features and tags by generating simultaneous semantic contexts. The experimental results demonstrate an improved accuracy over other single and multi-modal methods. Categories and Subject Descriptors H.3.1 [Content Analysis and Indexing]: Indexing methods; H.3.3 [Information Search and Retrieval]: Clustering, Information filt...
Chandrika Pulla, C. V. Jawahar
Added 15 Aug 2010
Updated 15 Aug 2010
Type Conference
Year 2010
Where CIVR
Authors Chandrika Pulla, C. V. Jawahar
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