Predicting the effectiveness of queries for visual search

Bing Li, Ling Yu Duan, Yiming Chen, Rongrong Ji, Wen Gao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


Poor retrieval performance significantly degenerates users' experience of visual search, especially in mobile search. Ideally, users would like to be alerted when bad queries are present, which helps eliminate latency as well as waste of bandwidth, especially in 3G wireless environment. In this paper, we propose a visual query performance prediction (v-QPP) approach to predict the retrieval effectiveness. We employ latent dirichlet allocation (LDA)to derive latent topics from image database. From the collection statistics, we model the query's specificity based on topics. High specificity helps a retrieval system to derive user's search intent exactly. Moreover, as low discriminative content is difficult to search in terms of distinguishing relevant images from irrelevant one, we propose a topics based inverse concept frequency (t-ICF) model to deal with specific queries but difficult to discriminate in the reference database. Comparison experiments over MPEG CDVS benchmarking datasets have shown our method significantly outperforms existing approaches in document retrieval. © 2012 IEEE.
Original languageEnglish (US)
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Number of pages4
StatePublished - Oct 23 2012
Externally publishedYes

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