Application Of Combination Of Multi-features And Support Vector Machine Ensemble To Image Classification

In recent years, with the explosive increase of image data, image classification has become a key task in many fields, so the study of image classification has great value. This thesis centers on effectively extracting image features and the design of classifiers for image classification. Moreover, a prototype system for image classification based on content is developed. The main contributions of this thesis are as follows:Since the single feature can only present partial contents of the image, which results in the insufficient distinguishing information, then the classification accuracy of image is not high. So here an approach for image classification based on combination of multi-features and support vector machine(SVM) is proposed. In this method, first feature of annular color histogram(ACH), feature of gray level co-occurrence matrix(GLCM), feature of tree-structured wavelet transform(TWT) and feature of edge direction histogram(EDH) are extracted respectively, then the extracted features are combined to form comprehensive features which can describe image content more completely and they are normalized with Gaussian normalization method. Finally, SVM is applied to classify images. Experimental results show that the accuracy of average classification of different kinds of images by this method is higher than that of the method based on single feature.Since a lot of redundant information of the extracted image features leads to the low classification accuracy of image, we introduce an approach for image classification based on combination of multi-features and principal component analysis RBaggSVM (PCA-RBaggSVM). In this method, first comprehensive features which can describe image content more completely are extracted, then their dimensions are reduced with PCA and reduced-dimension features are normalized with Gaussian normalization method. Finally, by manipulating training sets and SVM model parameters, a classifier of SVM ensemble is formed, which is used to classify images. Experimental results indicate that compared with BP Neural Network, C4.5 and RBaggSVM, this method can bring higher accuracy of average classification of different kinds of images and takes less total time in training and classifying of it.On the basis of above researched results, a prototype system for image classification based on content is designed and implemented. Testing results show that it operates correctly.