Research on Image Classification Based on a Combination of Text and Visual Features
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 testimage 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.
image classification text features visual features information fusion
Lexiao Tian Dequan Zheng Conghui Zhu
MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology Harbin, China
国际会议
上海
英文
1920-1924
2011-07-26（万方平台首次上网日期，不代表论文的发表时间）