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Image Ranking and Retrieval Based on Multi-Attribute Queries

8 years 7 months ago
Image Ranking and Retrieval Based on Multi-Attribute Queries
We propose a novel approach for ranking and retrieval of images based on multi-attribute queries. Existing image retrieval methods train separate classifiers for each word and heuristically combine their outputs for retrieving multiword queries. Moreover, these approaches also ignore the interdependencies among the query terms. In contrast, we propose a principled approach for multi-attribute retrieval which explicitly models the correlations that are present between the attributes. Given a multi-attribute query, we also utilize other attributes in the vocabulary which are not present in the query, for ranking/retrieval. Furthermore, we integrate ranking and retrieval within the same formulation, by posing them as structured prediction problems. Extensive experimental evaluation on the Labeled Faces in the Wild(LFW), FaceTracer and PASCAL VOC datasets show that our approach significantly outperforms several stateof-the-art ranking and retrieval methods.
Behjat Siddiquie, Rogerio Feris, Larry Davis
Added 20 Mar 2011
Updated 29 Apr 2011
Type Journal
Year 2011
Where CVPR
Authors Behjat Siddiquie, Rogerio Feris, Larry Davis
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