Soft SVM and its application to video object extraction

Support vector machines (SVM) are state-of-the-art learning machines and have found a great deal of success in a wide range of applications. In the framework of SVM, each sample belongs to either one class or the other. This requirement, however, makes it difficult to apply SVM to the applications where the data exhibit partial or unclear class memberships. To address this problem, this paper reformulates the standard SVM to be a new learning machine that is capable of dealing with binary (or hard) as well as real-valued (or soft) class memberships. The new machine, which is named soft SVM (S-SVM), has been integrated into a classification-based video object extraction approach, and the experimental results demonstrate the effectiveness of the new approach.