PicSOM—content-based image retrieval with self-organizing maps
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
An Adaptive Texture and Shape Based Defect Classification
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
IEEE Transactions on Circuits and Systems for Video Technology
MPEG-7 visual shape descriptors
IEEE Transactions on Circuits and Systems for Video Technology
The Evolving Tree—A Novel Self-Organizing Network for Data Analysis
Neural Processing Letters
A cartoon image classification system using MPEG-7 descriptors
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Analyzing large image databases with the evolving tree
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Personalized content presentation for virtual gallery
ICAT'06 Proceedings of the 16th international conference on Advances in Artificial Reality and Tele-Existence
Feature extraction and evaluation using edge histogram descriptor in MPEG-7
PCM'04 Proceedings of the 5th Pacific Rim conference on Advances in Multimedia Information Processing - Volume Part III
Shape-Based co-occurrence matrices for defect classification
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Contour co-occurrence matrix – a novel statistical shape descriptor
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
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In this paper the visual content descriptors defined by the MPEG-7 standard are applied to defect image classification and retrieval. A pre-classified defect image database is used in evaluation. The experiments are done with a KNN classifier and with a PicSOM content-based image retrieval system. Results indicate that the MPEG-7 features work with a high level of success, especially the Color Structure and Homogeneous Texture descriptors seem to perform well.