A Novel Image Classification Method Based on Manifold Learning and Gaussian Mizture Model

摘要：

Image classification is one of the important parts of digital image processing. We propose a novel feature space-based image classification method by combining manifold learning and mixture model. In this paper, the process of image classification can be viewed as two parts: a coarse-grained classification and a fine-grained classification. In the coarse-grained classification, we apply the ISOMAP (Isometric Mapping) algorithm to do a dimensional reduction based on manifold learning. Thus, solving the classification problem is transformed from a highdimensional data space to a low-dimensional feature space. And then, during the fine-grained classification, we present an improved EM algorithM of finite Gaussian mixture model to do clustering. Experimental results have demonstrated that the proposed method performs well in both accuracy and time. Additionally, our algorithm is robust to some extent.

关键词： Image classification Manifold learning Gaussian mizture model ISOMAP Dimension reduction

作者: Xianjun Zhang Min Yao Rong Zhu

作者单位: School of Computer Science & Technology, Zhejiang University Hangzhou, China State Key Laboratory fo School of Computer Science & Technology, Zhejiang University Hangzhou, China School of Computer Science & Technology, Zhejiang University Hangzhou, China School of Information E

会议类型: 国际会议

会议名称: 2010 International Conference on Image Analysis and Signal Processing(2010 图像分析与信号处理国际会议 IASP 10)

会议地点: 厦门

会议语种:英文

页码: 243-247

在线出版日期: 2010-04-12（万方平台首次上网日期，不代表论文的发表时间）