A relevance feedback mechanism for content-based image retrieval
Information Processing and Management: an International Journal
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modern Information Retrieval
Manifold-ranking based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Learning an image manifold for retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Formulating context-dependent similarity functions
Proceedings of the 13th annual ACM international conference on Multimedia
Enhancing relevance feedback in image retrieval using unlabeled data
ACM Transactions on Information Systems (TOIS)
Efficient top-k hyperplane query processing for multimedia information retrieval
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Query-Sensitive Similarity Measure for Content-Based Image Retrieval
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
ACM Computing Surveys (CSUR)
BALAS: Empirical Bayesian learning in the relevance feedback for image retrieval
Image and Vision Computing
Bridging the Gap: Query by Semantic Example
IEEE Transactions on Multimedia
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Mass estimation and its applications
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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This paper presents a ranking framework for content-based image retrieval using relevance feature mapping. Each relevance feature measures the relevance of an image to some profile underlying the image database. The framework is a two-stage process. In the off-line modeling stage, it constructs a collection of models which maps all images in the database to the relevance feature space. In the on-line retrieval stage, it assigns a weight to every relevance feature based on the query image, and then ranks images in the database according to their weighted average feature values. The framework also incorporates relevance feedback which modifies the ranking based on the feedbacks through reweighted features. We show that the power of the proposed framework is coming from the relevance features. Experiments on a large image database validate the efficacy and efficiency of the proposed framework.