关键词: 图像分类, 空间金字塔匹配, 兴趣域, 特征直方图, 平均准确度均值

Abstract: In the process of image classification, the image region containing object which plays a decisive role is indefinite in both position and scale, and it can not get a high accuracy of image classification by using Spatial Pyramid Matching（SPM） directly. Therefore, a method for improving the performance of image classification based on Region of Interest （ROI） detection and Spatial Pyramid Matching（SPM） is proposed. This method first makes use of localization results of detector, and it verifies feasibility of using a state-of-the-art object detection algorithm to separate the goals of image and background for image classification. Then, a method which is called coarse object alignment matching is used to construct spatial histogram features for these two regions separately based on SPM. Finally, the scores provided by real detector and Support Vector Machine(SVM) are combined to rescore for the final result. Experimental results demonstrate that the mean average precision of proposed method has been promoted over 12 percent than the standard SPM. Compared to three state-of-the-art methods, the proposed method gets the highest mean average precision, and gets the highest average precision at more than half image categories.

Key words: image classification, spatial pyramid matching, region of interest, spatial histogram features, mean average precision