关键词: 简单帧选择, 显著性检测, 多核SVM集成学习

Abstract: This paper proposes a novel video saliency detection method. Firstly, in order to extract features with high confidence in the video sequence, a simple frame selection criterion is proposed according to the initial saliency map of the input frame and the input frame, and the simple selection criterion is used to select the video sequence to easily and accurately extract the foreground object, it gets robust foreground background label from simple frames. Then the image is sub-pixel segmented, and the time-space feature and foreground tag input integrated learning model is extracted. After multi-kernel SVM integration learning, a pixel-level saliency map is finally generated, and the motion feature is spread to the entire video set. Experimental results of various video sequences show that the algorithm is superior to the traditional significant detection algorithm in qualitative and quantitative.

Key words: simple frame selection, saliency detection, multi-kernel SVM bootstrap learning