Saliency detection and feature matching for image trimming and tracking in active video

We develop a new automatic Object of Interest detection method for image trimming and a novel tracking technique in active videos. Both applications consist of salient region detection and feature matching. We deploy a color-saliency weighted Probability-of-Boundary (cPoB) map to detect salient regions. Scale Space Image Pyramid (SSIP) feature matching is proposed for image trimming. An image pyramid is created to imitate the view point change for stable keypoint selection. Successive Classification Maximum Similarities (SCMS) feature matching is used for tracking. A strong classifier trained by AdaBoost is utilized for keypoint classification and subsequent Linear Programming rejects outliers. The object-centered property of Active Video is highly beneficial because it captures the essence of Human Visual Attention and facilitates self-initialization in tracking. Experiments demonstrate the importance of saliency detection and feature matching and confirm that our approach can automatically detect salient regions in images and track reliably in videos.