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- W2379300631 abstract "AbstrcatA new method, based on support vector machines (SVMs) and genetic algorithm, is proposed for texture recognition. The main difficulty of texture recognition is the lack of effective tools to characterize different scales of textures. To alleviate the problem,wavelet co-occurrence parameters, window size, mean and standard deviation of different level discrete wavelet transform (DWT) images are used as texture features. In particular, we apply multi-class support vector machines for classification, using error-correcting output codes (ECOC) for the extension of binary SVMs. On the other hand, genetic algorithm is used for feature optimization to obtain more accurate rates. Compared to the conventional Bayes classifier, SVMs produce more accurate recognition rates on the Ponce texture database. The error rate of experiments is less than 10%." @default.
- W2379300631 created "2016-06-24" @default.
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- W2379300631 date "2005-01-01" @default.
- W2379300631 modified "2023-09-25" @default.
- W2379300631 title "Texture Recognition Using Support Vector Machines and Genetic Algorithm" @default.
- W2379300631 hasPublicationYear "2005" @default.
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