Title :
Study on the content-based image retrieval system by unsupervised learning
Author :
Shuo Wang ; Wang, Jun ; Bing Wang ; Xue-Zheng Wang
Author_Institution :
Machine Learning Center, Hebei Univ., Baoding, China
Abstract :
The content-based image retrieval (CBIR) system aims at searching and browsing the large image digital libraries based on automatically derived imagery features. This paper introduces two algorithms based on the normalized cut for images clustering. We extract the color and texture features for computing the distance between the images, and take advantage of the bipartition method and minimum spanning tree for grouping. The performance of this system using the above methods is evaluated on a database of around 8000 images from the internet. The searching accuracy is satisfied for the target requirement.
Keywords :
content-based retrieval; feature extraction; image colour analysis; image retrieval; image texture; pattern clustering; trees (mathematics); unsupervised learning; Internet; automatically derived imagery features; bipartition method; color features; content-based image retrieval system; feature extraction; image clustering; image digital libraries; minimum spanning tree; texture features; unsupervised learning; Content based retrieval; Cybernetics; Feature extraction; Histograms; Image color analysis; Image retrieval; Information retrieval; Machine learning; Shape; Unsupervised learning; Bipartition method; Color feature; Image retrieval system; Minimum spanning tree; Normalized cut; Texture feature;
Conference_Titel :
Machine Learning and Cybernetics, 2009 International Conference on
Conference_Location :
Baoding
Print_ISBN :
978-1-4244-3702-3
Electronic_ISBN :
978-1-4244-3703-0
DOI :
10.1109/ICMLC.2009.5212152
