Image ranking and retrieval based on multi-attribute queries

TitleImage ranking and retrieval based on multi-attribute queries
Publication TypeConference Papers
Year of Publication2011
AuthorsSiddiquie B, Feris RS, Davis LS
Conference NameComputer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on
Date Published2011/06//
Keywordsdatasets;image, datasets;PASCAL, faces, FaceTracer, in, methods;labeled, queries;image, ranking;image, retrieval, retrieval;, the, VOC, wild;multiattribute

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.