Ranking with local regression and global alignment for cross media retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Image categorization via robust pLSA
Pattern Recognition Letters
IEEE Transactions on Image Processing
Image Annotation Using Sub-block Energy of Color Correlograms
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
Multi-modal Correlation Modeling and Ranking for Retrieval
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Image retrieval based on multi-texton histogram
Pattern Recognition
Cross-media retrieval using query dependent search methods
Pattern Recognition
IPSILON: incremental parsing for semantic indexing of latent concepts
IEEE Transactions on Image Processing
Correlated PLSA for image clustering
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Image annotation based on central region features reduction
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Intelligent Social Media Indexing and Sharing Using an Adaptive Indexing Search Engine
ACM Transactions on Intelligent Systems and Technology (TIST)
Automatic image annotation with cooperation of concept-specific and universal visual vocabularies
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Cross-Media semantics mining based on sparse canonical correlation analysis and relevance feedback
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Multifeature analysis and semantic context learning for image classification
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Semantic Image Retrieval Using Collaborative Indexing and Filtering
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
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This paper addresses content-based image retrieval in general, and in particular, focuses on developing a hidden semantic concept discovery methodology to address effective semantics-intensive image retrieval. In our approach, each image in the database is segmented into regions associated with homogenous color, texture, and shape features. By exploiting regional statistical information in each image and employing a vector quantization method, a uniform and sparse region-based representation is achieved. With this representation, a probabilistic model based on statistical-hidden-class assumptions of the image database is obtained, to which the expectation-maximization technique is applied to analyze semantic concepts hidden in the database. An elaborated retrieval algorithm is designed to support the probabilistic model. The semantic similarity is measured through integrating the posterior probabilities of the transformed query image, as well as a constructed negative example, to the discovered semantic concepts. The proposed approach has a solid statistical foundation; the experimental evaluations on a database of 10 000 general-purposed images demonstrate its promise and effectiveness