Author: Xiang Fu, Sanjay Purushotham, Daru Xu, and C.-C. Jay Kuo

Rapid development of video sharing over the Internet creates a large number of videos every day. It is an essential task to organize or classify tons of Internet images or videos automatically online, which will be mostly helpful to the search of useful videos in the future, especially in the applications of video surveillance, image/video retrieval, etc.

One classic method to categorize videos for human is based on which makes content-based video analysis a hot topic. An object is undoubtedly the most significant component to represent the video content. Object recognition and classification plays a significant role for intelligent information processing.

The traditional tasks of object recognition and classification include two parts. One is to identify a particular object in an image from an unknown viewpoint given a few views of that object for training, which is called “multi-view specific object recognition”. Later on, researchers attempt to get the internal relation of object classes from one specific view, which develops to another task called “single-view object classification”. In this case, the object class diversity in appearance, shape, or color should be taken into consideration. These variations increase the difficulty in classification. Over the last decade, many researchers have solved the last two tasks using a concept called intra-class similarity. To further reduce the semantic gap between machine and human, the problem of “multi-view object classification” needs to be well studied.

As shown in Fig.1, there are three elements to define a view: angle, scale (distance), and height, which form the view sphere. Although the viewpoint as well as intra-class variations exist as illustrated in Fig.2, some common features can still be found for one object class by [...]