基于压缩直方图的图像分类

Image Classification Using Compressed Histograms

提出了一种颜色直方图表示的彩色图像自动归于语义类别的策略。在这个分类策略中,主成分分析用于把高维颜色直方图映射至低维特征空间,低维的特征向量用于训练支持向量机分类器。实验结果表明,尽管现有的多种图像内容描述子对分类效果的影响不同,但它们都是高度冗余的,可以在不影响分类正确性的基础上被大幅度压缩。本文研究有助于实现基于内容图像检索相关反馈所要求的快速在线学习和重新归类。

Organizing images are classified into semantic categories for searching and browsing through large image repositories. An efficient method is presented using various histogram-based (high-dimensional) image content descriptors for automatically classifying general color photos into relevant categories. Principal component analysis (PCA) is used to project the original high dimensional histograms on their eigenspaces. Lower dimensional eigenfeatures are used to train support vector machines (SVMs) to classify images into their categories. Experimental results show that even though different descriptors perform differently, they are all highly redundant. It is shown that the dimensionality of all these descriptors, regardless of their performances, can be significantly reduced without affecting classification accuracy. Such scheme is used an interactive setting for relevant feedback in content-based image retrieval, where low dimensional content descriptors enable fast online learning and reclassification of results.