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IEEE Transactions on Systems, Man and Cybernetics
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International Journal of Computer Vision
On the issue of obtaining OWA operator weights
Fuzzy Sets and Systems
Visual information retrieval
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Adaptive relevance feedback based on Bayesian inference for image retrieval
Signal Processing - Special section on content-based image and video retrieval
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ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
The CLEF 2004 cross-language image retrieval track
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
IEEE Transactions on Image Processing
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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Image Communication
Combining similarity measures in content-based image retrieval
Pattern Recognition Letters
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Relevance feedback based on genetic programming for image retrieval
Pattern Recognition Letters
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Apparel sizing using trimmed PAM and OWA operators
Expert Systems with Applications: An International Journal
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Fuzzy Sets and Systems
Multimedia retrieval in a medical image collection: results using modality classes
MCBR-CDS'12 Proceedings of the Third MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
An improved distance-based relevance feedback strategy for image retrieval
Image and Vision Computing
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Decision Support Systems
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This paper deals with the problem of image retrieval from large image databases. A particularly interesting problem is the retrieval of all images which are similar to one in the user's mind, taking into account his/her feedback which is expressed as positive or negative preferences for the images that the system progressively shows during the search. Here we present a novel algorithm for the incorporation of user preferences in an image retrieval system based exclusively on the visual content of the image, which is stored as a vector of low-level features. The algorithm considers the probability of an image belonging to the set of those sought by the user, and models the logit of this probability as the output of a generalized linear model whose inputs are the low-level image features. The image database is ranked by the output of the model and shown to the user, who selects a few positive and negative samples, repeating the process in an iterative way until he/she is satisfied. The problem of the small sample size with respect to the number of features is solved by adjusting several partial generalized linear models and combining their relevance probabilities by means of an ordered averaged weighted operator. Experiments were made with 40 users and they exhibited good performance in finding a target image (4 iterations on average) in a database of about 4700 images. The mean number of positive and negative examples is of 4 and 6 per iteration. A clustering of users into sets also shows consistent patterns of behavior.