Video Segmentation Based on Shot Boundary Coefficient

Recent advances in computing, communication, and sensor technology are pushing the development of many new applications. This trend is especially evident in pervasive computing. The content-based video analysis is one example of this innovation. Shot boundary detection servers as an important step to structure the contents of videos, many research efforts have been devoted to this field. A novel approach is proposed to detect cuts and gradual shots. An effective feature selection mechanism is introduced using shot boundary coefficient (SBC) combined with HSV divisional histogram. SBC is derived from a model called average sliding-window difference. It is a novel way to enlarge frame difference. SBC can remove the factors affected by camera/object motion, illumination and other artifacts on the curve of frame-wise histogram difference. Each area of the frame has different weight according to its respective importance. In order to improve the speed, at the beginning of the program, we adopt n rank Newton interpolation formula to deflate the frame size. We use Self-Organizing Map (SOM) Network to detect the abrupt shot. It can avoid the threshold completely. Gauss Model is selected to detect gradual shots. Based on the set of abrupt shots, we qualify the candidate set of the gradual shot furthermore. The experimental results have shown that a good compromise between speed and accuracy is achieved. It also has good noise tolerance characteristics.