Wavelets for computer graphics: theory and applications
Wavelets for computer graphics: theory and applications
Wavelets and imaging informatics: a review of the literature
Computers and Biomedical Research
Modern Information Retrieval
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
The Amsterdam Library of Object Images
International Journal of Computer Vision
Fractal Analysis of Image Textures for Indexing and Retrieval by Content
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Constraining and summarizing association rules in medical data
Knowledge and Information Systems
Proceedings of the 2008 ACM symposium on Applied computing
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Mining interesting association rules in medical images
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
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The "gap semantic" and the "curse of dimensionality" are two shortcomings of content-based image retrieval techniques that rely on automatic feature extracted from images to process similarity queries. The first one represents the semantic gap that exists between low-level features automatically extracted by a computational system, and the high-level user interpretation of images. The second one involves problems occurring when similarity is defined over high-dimensional feature spaces. This paper shows a method that deals with these both shortcomings. We use discrete wavelet transforms to obtain the image representation from a multiresolution point of view. The feature vectors were composed of the features from the approximation subspace, which succinctly represent the images in the processing of similarity queries. In addition, the multiresolution method was used to reduce the dimensionality of the feature space. This work shows the evaluation of three different image datasets, where the first two are composed of medical images and the third one is a generic image dataset. The results are promising and show an improvement of up to 90% for recall values up to 65%, in the query results using the Daubechies wavelet transform.