Multimodal pLSA on visual features and tags
This work studies a new approach for image retrieval on largescale community databases. Our proposed system explores two different modalities: visual features and communitygenerated 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%.