Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
Learning to cluster web search results
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
IGroup: web image search results clustering
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Bipartite graph reinforcement model for web image annotation
Proceedings of the 15th international conference on Multimedia
Dual cross-media relevance model for image annotation
Proceedings of the 15th international conference on Multimedia
Hierarchical clustering-based navigation of image search results
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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The performance of traditional image retrieval approaches remains unsatisfactory, as they are restricted by the well-known semantic gap and the diversity of textual semantics. To tackle these problems, we propose an improved image retrieval framework when querying with an image. The framework considers not only the discriminative power of various visual properties but also the semantic representation of the query image. Given a query image, we first perform CBIR to obtain some visually similar image sets corresponding to different visual properties separately. Then, a semantic representation to the query image is learnt from each image set. The semantic consistence among the textual indexes of each image set is measured in order to judge the confidence of various visual properties and the obtained semantic representation in search. Obtaining these items, both visually and semantically relevant images are returned to the user by a combined similarity measure. Experiments on a large-scale web images demonstrate the effectiveness and potential of the proposed framework.