Scalable relevance feedback using click-through data for web image retrieval
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Semantic image retrieval based on probabilistic latent semantic analysis
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Proceedings of the international workshop on Workshop on multimedia information retrieval
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ACM Computing Surveys (CSUR)
Cross-media manifold learning for image retrieval & annotation
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
A human computer integrated approach for content based image retrieval
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
POCS-Based Annotation Method Using Kernel PCA for Semantic Image Retrieval
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Unsupervised active learning based on hierarchical graph-theoretic clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A unified relevance feedback framework for web image retrieval
IEEE Transactions on Image Processing
Multimodal image retrieval via Bayesian information fusion
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Multimedia multimodal methodologies
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Joint querying and relevance feedback scheme for an on-line image retrieval system
AIKED'10 Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Evolutionary cross-domain discriminative hessian eigenmaps
IEEE Transactions on Image Processing
On-line content-based image retrieval system using joint querying and relevance feedback scheme
WSEAS Transactions on Computers
Local-feature-based image retrieval with weighted relevance feedback
International Journal of Business Intelligence and Data Mining
Methods for automatic and assisted image annotation
Multimedia Tools and Applications
A probability-based unified 3d shape search
SAMT'06 Proceedings of the First international conference on Semantic and Digital Media Technologies
Optimal matching of images using combined color feature and spatial feature
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
Unifying keywords and visual features within one-step search for web image retrieval
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
A unified context model for web image retrieval
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Bidirectional-isomorphic manifold learning at image semantic understanding & representation
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Using Hilbert scan on statistical color space partitioning
Computers and Electrical Engineering
A new ROI based image retrieval system using an auxiliary Gaussian weighting scheme
Multimedia Tools and Applications
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In this paper, a unified image retrieval framework based on both keyword annotations and visual features is proposed. In this framework, a set of statistical models are built based on visual features of a small set of manually labeled images to represent semantic concepts and used to propagate keywords to other unlabeled images. These models are updated periodically when more images implicitly labeled by users become available through relevance feedback. In this sense, the keyword models serve the function of accumulation and memorization of knowledge learned from user-provided relevance feedback. Furthermore, two sets of effective and efficient similarity measures and relevance feedback schemes are proposed for query by keyword scenario and query by image example scenario, respectively. Keyword models are combined with visual features in these schemes. In particular, a new, entropy-based active learning strategy is introduced to improve the efficiency of relevance feedback for query by keyword. Furthermore, a new algorithm is proposed to estimate the keyword features of the search concept for query by image example. It is shown to be more appropriate than two existing relevance feedback algorithms. Experimental results demonstrate the effectiveness of the proposed framework.