Supporting similarity queries in MARS
MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
Surfimage: a flexible content-based image retrieval system
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
A Region-Based Representation of Images in MARS
Journal of VLSI Signal Processing Systems - special issue on multimedia signal processing
NeTra: a toolbox for navigating large image databases
Multimedia Systems - Special issue on video content based retrieval
Shape representation for image retrieval
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 2)
Semantic based image retrieval: a probabilistic approach
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
A statistical correlation model for image retrieval
MULTIMEDIA '01 Proceedings of the 2001 ACM workshops on Multimedia: multimedia information retrieval
Content-based visual information retrieval
Distributed multimedia databases
MUSE: A Content-Based Image Search and Retrieval System Using Relevance Feedback
Multimedia Tools and Applications
Supporting Ranked Boolean Similarity Queries in MARS
IEEE Transactions on Knowledge and Data Engineering
Using a Relevance Feedback Mechanism to Improve Content-Based Image Retrieval
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
Retrieving Images by Content: The Surfimage System
MIS '98 Proceedings of the 4th International Workshop on Advances in Multimedia Information Systems
eID: a system for exploration of image databases
Information Processing and Management: an International Journal
Efficient Query Refinement for Image Retrieval
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Content-Based Image Retrieval Based on a Fuzzy Approach
IEEE Transactions on Knowledge and Data Engineering
Interactive exploration for image retrieval
EURASIP Journal on Applied Signal Processing
Mixture of KL subspaces for relevance feedback
Multimedia Tools and Applications
Computer Vision and Image Understanding
Adaptive multiple feedback strategies for interactive video search
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
A New Approach to Interactive Visual Search with RBF Networks Based on Preference Modelling
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Advanced Information Retrieval
Electronic Notes in Theoretical Computer Science (ENTCS)
Texture Retrieval Effectiveness Improvement Using Multiple Representations Fusion
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Multimedia Tools and Applications
Information retrieval from visual databases using multiple representations and multiple queries
Proceedings of the 2009 ACM symposium on Applied Computing
Hidden semantic concept discovery in region based image retrieval
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
VisionGo: Towards video retrieval with joint exploration of human and computer
Information Sciences: an International Journal
Assessing the quality of textual features in social media
Information Processing and Management: an International Journal
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Content-based multimedia information retrieval (MIR) has become one of the most active research areas in the past few years. Many retrieval approaches based on extracting and representing visual properties of multimedia data have been developed. While these approaches establish the viability of MIR based on visual features, techniques for incorporating human expertise directly during the query process to improve retrieval performance have not drawn enough attention. To address this limitation, this paper introduces a Human-Computer Interaction based approach to MIR in which the user guides the system during retrieval using relevance feedback. Our experiments show that the retrieval performance improves significantly by incorporating humans in the retrieval process.