Notice of Violation of IEEE Publication PrinciplesGeneric Object Recognition Via Integrating Distinct Features with SVM

Notice of Violation of IEEE Publication Principles

"Generic Object Recognition Via Integrating Distinct Features with SVM"
by Tong-Cheng Huang and You-Dong Ding
in Proceedings of 2006 International Conference on Machine Learning and Cybernetics, pp 3897-3902.

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE\´s Publication Principles.

This paper is a near duplication of the original text from the papers cited below. The original text was copied without attribution and without permission.

Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following articles:

"Generic Object Recognition by Combining Distinct Features in Machine Learning,"
by Hongying Meng, David R. Hardoon, John Shawe-Taylor, Sandor Szedmak,
in the Proceedings of the 17th Annual Symposium on Electronic Imaging, January 2005, SPIEIn a generic image object recognition or categorization system, the relevant features or descriptors from a characteristic point, patch or region of an image are often obtained by different approaches. And these features are often separately selected and learned by machine learning methods. In this paper, the relation between distinct features obtained by different feature extraction approaches and that for the same original images were studied by kernel canonical correlation analysis (KCCA). We apply a support vector machine (SVM) classifier in the learnt semantic space of the combined features and compare against SVM on the raw data and previously published state-of-the-art results. Experiments show that significant improvement is achieved with the SVM in the semantic space in comparison with direct SVM classification on the raw data