Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Fusing MPEG-7 visual descriptors for image classification
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
IEEE Transactions on Circuits and Systems for Video Technology
MPEG-7 visual shape descriptors
IEEE Transactions on Circuits and Systems for Video Technology
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MPEG-7 visual descriptors include the color, texture and shape descriptor and were introduced, after a long period of evaluation, for efficient content-based image retrieval. A total of 22 different kind of features are included, nine for color, eight for texture and five for shape. Encoded values of these features vary significantly and their combination, as a means for better retrieval, is neither straightforward nor efficient. Despite their extensive usage MPEG-7 visual descriptors have never compared concerning their retrieval performance; thus the question which descriptor to use for a particular image retrieval scenario stills unanswered. In this paper we report the results of an extended experimental study on the efficiency of the various MPEG-7 visual features with the aid of the Weka tool and a variety of well-known data classifiers. Our data consist of 1952 images from the athletics domain, containing 7686 manually annotated objects corresponding to eight different classes. The results indicate that combination of selected MPEG-7 visual features may lead to increased retrieval performance compared to single descriptors but this is not a general fact. Furthermore, although the models created using alternative training schemes have similar performance libSVM is by far more effective in model creation in terms of training time and robustness to parameter variation.