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As more and more text-image co-occurrence data become available on the web, mining on those data is playing an increasingly important role in web applications. In this paper, we consider utilizing description information to help image classification and propose a novel image classification method focusing on text-image co-occurrence data. In general, there are three main steps in our system: feature extraction, training classifiers and classifier fusion. In feature extraction phase, several features are extracted including not only visual features such as color, shape, texture, but also text features. In the process of training classifiers, visual and text classifiers are trained separately with SVM model. Finally, Weight learning is used to build the classifier fusion system. Comparing with other methods, we make full use of unstructured texts around images and filter text features through information gain, also efficient combination of features is achieved by comparing different combination methods. Experimental results show that our method is efficient and enhances the accuracy of image classification.