Image Labeling using Integration of Local and Global Features

摘要

In this paper, we carry out image labeling based on probabilistic integration of local and global features. Many conventional methods put label to each pixel or region using the features extracted from local regions and local contextual relationships between neighboring regions. However, labeling results tend to depend on a local viewpoint. To overcome this problem, we propose the image labeling method using not only local features but also global features. We compute posterior probability of local and global features independently, and they are integrated by the product. To compute probability of global region (entire image), Bag-of-Words is used. On the other hand, local co-occurrence between color and texture features is used to compute local probability. In the experiments using MSRC21 dataset, labeling accuracy is much improved by using global viewpoint.