Handwriting recognition based on SVM
2016-08-23
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&Image segmentation is an important research content of image processing and the first step of image analysis. The purpose of image segmentation is to extract the object people care about from the image. At present, many domestic and foreign scholars have proposed a variety of image segmentation methods to solve this problem, but these methods can not be generally applied to all kinds of images, so the general segmentation methods are only effective for specific images. Support vector machine (SVM) is a classification method based on statistical learning theory, which has been widely used in many fields, such as pattern recognition, data classification, image segmentation and so on. Support vector machine (SVM) is a kind of classification algorithm with strong generalization ability, so it has become a common trend to apply SVM algorithm to image segmentation, and can obtain good segmentation results. The essential idea of image segmentation method based on SVM is classification. It uses the gray information or other features of pixels in the image as the feature attributes of training samples to train SVM classifier, and then uses the trained classifier to segment the image. However, because SVM algorithm is a supervised classification algorithm, when it is applied to image segmentation, people need to select suitable and appropriate training samples for SVM model. However, there are certain subjectivity and randomness in the selection of training samples, which is time-consuming and laborious, and can not obtain satisfactory segmentation results. Therefore, how to automatically select well distributed and appropriate training samples and make the training samples widely represent the sample points will become the research focus of image segmentation based on support vector machine. Aiming at the problem of selecting training samples in SVM based image segmentation method, this paper proposes two SVM color image segmentation methods which can automatically obtain training samples and label the classification automatically. The main work of this paper includes: (1) the image segmentation method based on support vector machine is deeply studied
matlab
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svm
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手写
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