Incremental feature weight learning and its application to a shape-based query system
Pattern Recognition Letters
Integrating Relevance Feedback Techniques for Image Retrieval Using Reinforcement Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incorporating real-valued multiple instance learning into relevance feedback for image retrieval
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
An Integrated Color and Intensity Co-occurrence Matrix
Pattern Recognition Letters
Computer Vision and Image Understanding
Image Retrieval: Color and Texture Combining Based on Query-Image
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Semantic retrieval of events from indoor surveillance video databases
Pattern Recognition Letters
Logistic regression models for a fast CBIR method based on feature selection
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Distribution-based similarity for multi-represented multimedia objects
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
Series feature aggregation for content-based image retrieval
Computers and Electrical Engineering
Towards more effective distance functions for word image matching
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Pattern Recognition Letters
Feature re-weighting in content-based image retrieval
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
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Content-based image retrieval systems use low-level features like color and texture for image representation. Given these representations as feature vectors, similarity between images is measured by computing distances in the feature space. Unfortunately, these low-level features cannot always capture the high-level concept of similarity in human perception. Relevance feedback tries to improve the performance by allowing iterative retrievals where the feedback information from the user is incorporated into the database search. We present a weighted distance approach where the weights are the ratios of standard deviations of the feature values both for the whole database and among the images selected as relevant by the user. The feedback is used for both independent and incremental updating of the weights and these weights are used to iteratively refine the effects of different features in the database search. Retrieval performance is evaluated using average precision and progress that are computed on a database of approximately 10,000 images and an average performance improvement of 19% is obtained after the first iteration.