IEEE Transactions on Pattern Analysis and Machine Intelligence
Textural Features for Image Database Retrieval
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Image Segmentation Using Local Variation
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Fuzzy Relational Distance for Large-Scale Object Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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Feature vectors that are used to represent images exist in a very high dimensional space. Usually, a parametric characterization of the distribution of this space is impossible. It is generally assumed that the features are able to locate visually similar images close in the feature space so that non-parametric approaches, like the k-nearest neighbor search, can be used for retrieval. This paper introduces a graph-theoretic approach to image retrieval by formulating the database search as a graph clustering problem to increase the chances of retrieving similar images by not only ensuring that the retrieved images are close to the query image, but also adding another constraint that they should be close to each other in the feature space. Retrieval precision with and without clustering are compared for performance characterization. The average precision after clustering was 0.78, an improvement of 6.85% over the average precision before clustering.