摘要：

基于内容的图像快速分类是Web图像实时搜索和过滤的基础。通过分析图像特征分布特点，提出一个基于局部特征的图像快速分类算法。与目前算法相比，该算法仅需对图像的局部区域扫描分析，即可得到其颜色、纹理、形状等特征，并利用Bayesian分类器来实现图像的快速自动分类。相关对比实验证实，该算法能够快速、准确地实现图像分类。

关键词: 局部特征, 图像, 贝叶斯分类

Abstract: The fast classification of image based on content is the groundwork for real-time searching and filtration of Web image. After the analysis of the distributing characteristics for image feature, a fast image classification algorithm based on local feature is proposed. Compared with the algorithms at present, the features like color, texture, shape are got just in local area of images. And then, images can be fast and automatically classified using Bayesian classifier. In this paper, the algorithm is implemented and the result is improved that the classification is fast and efficient by contrastive experiments.

Key words: local feature, image, Bayesian classification

中图分类号: 

• TP391.41