Video object (VO) extraction is of great importance in multimedia processing. In recent years approaches have been proposed to deal with VO extraction as a classification problem. This type of methods calls for state-of-the-art classifiers because the performance is directly related to the accuracy of classification. Promising results have been reported for single object extraction using support vector machines (SVM) and its extensions. Multiple object extraction, on the other hand, still imposes great difficulty as multi-category classification is an ongoing research topic in machine learning. This paper introduces a new scheme of multi-category learning for multiple VO extraction, and demonstrates its effectiveness and advantages by experiments.
Bibliographical noteFunding Information:
This work was supported in part by the US National Science Foundation under Grant IIS-0328802, and in part by the Chinese Natural Science Foundation under Grant 60632040.
- Multi-class classification
- Multiple object tracking
- Support vector machines (SVM)
- VO extraction