The Community for Technology Leaders
CVPR 2011 (2011)
Providence, RI
June 20, 2011 to June 25, 2011
ISBN: 978-1-4577-0394-2
pp: 801-808
B. Siddiquie , Univ. of Maryland, College Park, MD, USA
L. S. Davis , Univ. of Maryland, College Park, MD, USA
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 state-of-the-art ranking and retrieval methods.
PASCAL VOC datasets, image ranking, multiattribute queries, image retrieval methods, labeled faces in the wild, FaceTracer datasets

L. S. Davis, B. Siddiquie and R. S. Feris, "Image ranking and retrieval based on multi-attribute queries," CVPR 2011(CVPR), Providence, RI, 2011, pp. 801-808.
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