Video object encoder using selective local-space support vector machines

Recently, support vector machine (SVM) has been shown to be a good classifier; however, its large computational requirement prohibited its use in real time video processing applications. In this paper, a model is proposed that enables use of SVM in video applications. The proposed model allows selected image scales (of interest) to be encoded and classified more accurately by complex classifier such as SVM, whilst other image scales of less significance to be encoded and classified by simpler encoder/classifier. Experiment with video object encoding shows that the performance of the proposed model is comparable with other models, however with reduced computational requirements.