关键词: 加速稳健特征（SURF）, 图像分割, 图像分类, 自学习, 主动轮廊模型

Abstract: In the image classification process, the classification accuracy is affected by the randomicity of feature point selection. In order to solve this problem, a self-learning classification algorithm based on image object feature space is proposed. The image target area is found with the local multi-channel active contour model based on the colour and texture feature. Features are selected in the target area and it makes sparse coding on features, which is used to establish the feature space. In order to further improve the classification accuracy, a voting mechanism is established on the proposed algorithm. Experimental results indicate that the proposed algorithm can avoid the impact of the randomicity of feature point selection, and effectively improve the accuracy of image classification.

Key words: Speeded Up Robust Features（SURF）, image segmentation, image classification, self learning, active contour model