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An improved image classification method basd on multi features using fuzzy support vector machine

机译:一种改进的基于模糊支持向量机的多特征图像分类方法

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The development of electronic technology and multimedia technology has led to the rapid growth of digital images. It has become an important problem to be solved to depend on advanced technology to classify images. We proposed an image classification method based on multi features using fuzzy support vector machine. It extractes various features and uses fuzzy support vector machine to classify images. The proposed method overcomes the shortcoming of traditional support vector machine in multi-classification problems and solves the problem of semantic ambiguity in image classification field by using fuzzy membership function. Using 6 types of natural images to train and test, the experiment results show that classification performance improves significantly compared with the traditional support vector machine algorithm. It lays a good foundation for further improving digital image understanding.
机译:电子技术和多媒体技术的发展导致数字图像的快速增长。依靠先进技术对图像进行分类已经成为一个重要的问题。提出了一种基于模糊支持向量机的多特征图像分类方法。它提取各种特征并使用模糊支持向量机对图像进行分类。该方法克服了传统支持向量机在多分类问题上的缺点,并通过模糊隶属度函数解决了图像分类领域的语义歧义问题。实验结果表明,与传统的支持向量机算法相比,利用6种自然图像进行训练和测试,分类性能明显提高。它为进一步提高对数字图像的理解奠定了良好的基础。

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