An Outdoor Scene Understanding Method Based on Ensemble Classification of Image Regions
一种基于图像区域系综分类的室外场景理解方法

Even multi-layer perception (MLP) classifier has been an efficient method of data classification, and the performance is often limited by the training samples space. In this paper, the MLP classifiers ensemble is used to improve the performance of image region classification in understanding of outdoor scene and a scheme for automated recognition concept classes of objects in outdoor scene images by image region classification is presented. First, the low-level visual features are extracted from the segmented image region, and then the ensemble classifiers are used to establish corresponding relationship between the visual features of image region and semantic class. Finally, the high-level semantic class of each object in an image is formed by combining the region with same label. The method has been evaluated on 150 images including five objects and recognition rate is around 87%. The experimental results show that the proposed method that has better performance compared to MLP-based method is suitable for image regions classification. Moreover, this ensemble method appears to generalize to other classification problems. (