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This paper proposes an image semantic classification algorithm based on feature subspaces. It is implemented by SVM and AdaBoost algorithm. In every feature subspace, a SVM is trained. According to the error rate of every SVM, the integrating weight of feature subspace is determined, with which different subspace features are concatenated into a feature vector. Then AdaBoost algorithm is employed...
This paper proposes an image retrieval method based on multi-feature similarity score fusion using genetic algorithm. Single feature describes image content only from one point of view, which has a certain one-sided. Fusing multi-feature similarity score is expected to improve the system's retrieval performance. In this paper, the retrieval results from color feature and texture feature are analyzed,...
A heritage debris classification based on 3D texture feature and SVM is proposed in this paper. We compute the bidirectional histogram and correlation function of Lambertian, isotropic, randomly rough surfaces which are common in real-world scenes firstly, and final classification results are gained with SVM. Performance is evaluated by employing 600 texture images corresponding to 61 real-world surface...
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