An approach to the automatic construction of global thesauri
Information Processing and Management: an International Journal
A fuzzy document retrieval system using the keyword connection matrix and a learning method
Fuzzy Sets and Systems - Special issue on applications of fuzzy systems theory, Iizuka '88
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Local Feedback in Full-Text Retrieval Systems
Journal of the ACM (JACM)
Improving the effectiveness of information retrieval with local context analysis
ACM Transactions on Information Systems (TOIS)
A unified framework for semantics and feature based relevance feedback in image retrieval systems
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Theory of keyblock-based image retrieval
ACM Transactions on Information Systems (TOIS)
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Unifying Keywords and Visual Contents in Image Retrieval
IEEE MultiMedia
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
A survey on the use of relevance feedback for information access systems
The Knowledge Engineering Review
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Semantic Modeling of Natural Scenes for Content-Based Image Retrieval
International Journal of Computer Vision
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Review: Which is the best way to organize/classify images by content?
Image and Vision Computing
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
A unified image retrieval framework on local visual and semantic concept-based feature spaces
Journal of Visual Communication and Image Representation
Proceedings of the ACM International Conference on Image and Video Retrieval
Which is the best multiclass SVM method? an empirical study
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
An efficient and effective region-based image retrieval framework
IEEE Transactions on Image Processing
Overview of the MPEG-7 standard
IEEE Transactions on Circuits and Systems for Video Technology
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We present an image retrieval framework based on automatic query expansion in a concept feature space by generalizing the vector space model of information retrieval. In this framework, images are represented by vectors of weighted concepts similar to the keyword-based representation used in text retrieval. To generate the concept vocabularies, a statistical model is built by utilizing Support Vector Machine (SVM)-based classification techniques. The images are represented as ''bag of concepts'' that comprise perceptually and/or semantically distinguishable color and texture patches from local image regions in a multi-dimensional feature space. To explore the correlation between the concepts and overcome the assumption of feature independence in this model, we propose query expansion techniques in the image domain from a new perspective based on both local and global analysis. For the local analysis, the correlations between the concepts based on the co-occurrence pattern, and the metrical constraints based on the neighborhood proximity between the concepts in encoded images, are analyzed by considering local feedback information. We also analyze the concept similarities in the collection as a whole in the form of a similarity thesaurus and propose an efficient query expansion based on the global analysis. The experimental results on a photographic collection of natural scenes and a biomedical database of different imaging modalities demonstrate the effectiveness of the proposed framework in terms of precision and recall.