Cut detection using block based center symmetric local binary pattern

Due to the popularity of multimedia technology and digital world, thousands of videos are accessed through internet in seconds. Most of the videos, available in internet for public access are non-edited videos. Efficient way of searching and storage need an efficient method of annotation. Automatic cut detection is the first stage of any automatic annotation process. In this paper we addressed the problem of video segmentation of only non-edited videos by classifying the boundary and non-boundary frames. The efficiency of intensity based cut detection methods decrease with variation of intensity of the scene. The local binary pattern is one of the texture feature which provides a strong spatial correlation among the neighboring pixels, which is also invariant to light variation. Therefore in the proposed method, the block based center symmetric local binary pattern feature vector is used for the detection of shot boundaries in a video. The Euclidean distance between the consecutive frame's feature vector is chosen as the similarity measure which is compared with a threshold value to detect the hard cuts in a non-edited video. The proposed algorithm is experimented with seven test videos and its efficacy is validated with few existing popular approaches.