Multiple video object extraction using multi-category ψ-learning

As a requisite of content-based multimedia technologies, video object (VO) extraction is of great importance. In recent years, approaches have been proposed to handle VO extraction directly as a classification problem. This type of methods calls for state-of-the-art classifiers because the extraction 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 such as ψ-learning. Multiple object extraction, on the other hand, still imposes great difficulty as multi-category classification is an on-going research topic in machine learning. This paper introduces the newly developed multi-category ψ-learning as the multi-class classifier for multiple VO extraction, and demonstrates its effectiveness and advantages by experiments.