关键词: 图像分类, 可分性, 距离度量, 决策属性, 最近邻分类

Abstract: Similarity measurement of image classification leads to outliers caused by vague boundary of feature points. To address this problem, an image classification method based on feature point class separability criterion is proposed. This paper combines information entropy theory used to extract the separability characteristics of image features. The separability distance values between feature vectors are calculated, and the nearest neighbor feature vector set is obtained according to the different nature of marking decision attribute by feature vectors. A new image type criterion is defined from two ways, the one is average distance between every feature vector of test image and the marking image class by nearest neighbor feature vector set, the other is average separability measurement value. The theoretical analysis and the Caltech256 simulation experiment show that compared with other methods, the feature point class separability criterion improves the image classification accuracy effectively.

Key words: image classification, separability, distance measurement, decision attribute, nearest neighbor classification