Universal approximation using radial-basis-function networks
Neural Computation
Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Principles of visual information retrieval
Principles of visual information retrieval
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
A Relevance Feedback Architecture for Content-based Multimedia Information Retrieval Systems
CAIVL '97 Proceedings of the 1997 Workshop on Content-Based Access of Image and Video Libraries (CBAIVL '97)
Edge Flow: A Framework of Boundary Detection and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Strategies for Positive and Negative Relevance Feedback in Image Retrieval
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
A Multiple-Instance Neural Networks based Image Content Retrieval System
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 2
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
A two-level relevance feedback mechanism for image retrieval
Expert Systems with Applications: An International Journal
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In this paper we propose a new method for image retrieval with relevance feedback based on eliciting preferences from the decision-maker acquiring visual information from an image database. The proposed extension of the common approach to image retrieval with relevance feedback allows it to be applied to objects with non-homogenous colour and texture. This has been accomplished by the algorithms, which model user queries by an RBF neural network. As an example of application of this approach, we have used a content-based search in an atlas of species. An experimental comparison with the commonly used content-based image retrieval approach is presented.