Image understanding technique is an important topic in computer vision. It has many applications and thus receives extensive attention in academia and industry. In this thesis, we explore how image region annotation, image retrieval and image editing can benefit from region analysis. Region is an intermediate semantic unit between the local feature points and global image. Compared to the local feature points, the region is able to maintain the richer semantics, and to some extent reduce the semantic gap; compared to the global image, the region is more flexible, and can serve as a partial solution to the image registration problem. Region is always obtained by image segmentation or object detection, and the high-dimensional features extracted from the image region generally contain noise and redundancy. How to extract the most informative feature subset and filter out a large number of irrelevant features is also a problem. Although region analysis has played a very important role in image understanding, research in this area is scary. Therefore, this thesis comprehensively considers the applications of the region analysis in image understanding from the following four aspects: (1) Automatic image feature selection. In recent years, different kinds of features, such as color, texture and shape features have shown to be able to enhance the performance of computer vision systems. However, current mobile systems still suffer from limited storage space and computing power. Therefore, how to effectively select a small amount of the most critical features without degrading the system's performance is a problem to be solved. (2) Automatic image region annotation. As more and more images with user-generated tags are available in the internet, how to automatically infer image pixel's tags based on the image tags attracts growing attentions. Because image and pixels lie in different levels, the cross-level tag transfer task is extremely difficult. (3) Cross-scenario image retrieval. Image retrieval is an important branch of image understanding. Most existing work focus on image retrieval within one domain. How to guarantee the search accuracy when query image and database lie in different domain is a challenging problem. (4) Automatic image editing. Image editing, due to its potentially huge market value, has received great attention from industry. However, currently the applicable image editing is manually done by experts with professional software. How to...
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