Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Multimedia Systems - Special issue on content-based retrieval
Handbook of image processing operators
Handbook of image processing operators
The pyramid-technique: towards breaking the curse of dimensionality
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
NeTra: a toolbox for navigating large image databases
Multimedia Systems - Special issue on video content based retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance evaluation in content-based image retrieval: overview and proposals
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
Filter Image Browsing: Interactive Image Retrieval by Using Database Overviews
Multimedia Tools and Applications
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Content-Based Image Retrieval by Relevance Feedback
VISUAL '00 Proceedings of the 4th International Conference on Advances in Visual Information Systems
Integrated Browsing and Searching of Large Image Collections
VISUAL '00 Proceedings of the 4th International Conference on Advances in Visual Information Systems
Using Contextual Information for Image Retrieval
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
Improving image retrieval performance with negative relevance feedback
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Content based indexing of images and video using face detection and recognition methods
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
IEEE Transactions on Image Processing
PicToSeek: combining color and shape invariant features for image retrieval
IEEE Transactions on Image Processing
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The ultimate objective of image retrieval research is to improve the user experiences in dealing with images, which happens to be closely related not only to the retrieval accuracy but also to the way he/she interacts with the retrieval tools. In particular, recognizing the subjectivity inherent in the problem leads to more emphasis on active participation of humans. This paper proposes a new user-system interaction model in the context of similarity retrieval of images.In the proposed model, the interaction is pursued to the extreme so that it tries to help users to browse huge image space with ease and efficiency rather than to find a certain images automatically on behalf of users. The system dynamically reconstructs the view reflecting user commands, while the user continuously modifies his/her commands while seeing the constantly changing view. Here the user's command is called a hint to distinguish it from a query, which is also a representation of user intention, but in a more formalized and complete form. Hints include all the intermediary steps of the user intention description process. By reflecting the intermediary steps to the view immediately, users receive feedback information to modify their description. This gradual and evolutionary process has a huge advantage over traditional approaches, especially when the user intention itself is ambiguous, which is often the case in realistic situations.This paper also describes a simple and efficient multi-dimensional feature indexing algorithm as an enabling technology to ensure immediate response. The algorithm transforms multi-dimensional features to one or more scalar values, which are used for restricting the search space. The algorithm proved to be efficient in realistic situations by being tested on the implementation of the proposed model.