Multimodal pLSA on Visual Features and Tags

Multimodal pLSA on Visual Features and Tags

erschienen 27.05.09 Technical Report, Institute of Computer Science, University of Augsburg, May 2009

ABSTRACT

This work studies a new approach for image retrieval on largescale community databases. Our proposed system explores two different modalities: visual features and community generated metadata, such as tags. We use topic models to derive a high-level representation appropriate for retrieval for each of our images in the database. We evaluate the proposed approach experimentally in a query-by-example retrieval task and compare our results to systems relying solely on visual features or tag features. It is shown that the proposed multimodal system outperforms the unimodal systems by approximately 36%.

 

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