IMAGE CLASSIFICATION BY COMBINING MULTIPLE SVMS

摘要：

In this paper, a novel framework is proposed for classifying images, which integrates several sets of Support Vector Machines(SVM) on multiple low level image features. In the proposed framework several global image features are extracted from the input images, and SVM using linear kernel with probability outputs are constructed on each feature. The outputs of the SVM classifiers are then combined by gλ-fuzzy integral. The density value of the fuzzy integral for each classifier is trained by using grid searching algorithm. Compared with some current systems, our proposed framework demonstrates a promising performance for an image database of general-purpose images from Corel image library.

关键词： Image classification Support vector machines Fuzzy integral Global image feature

作者: DE-YUAN ZHANG BING-QUAN LIU XIAO-LONG WANG LI-JUAN WANG

作者单位: School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China Machine Learning Center, Faculty of Mathematics and Computer Science, Hebei University, Baoding 0710

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 68-73

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）