Semantic Grouping of Shots in a Video Using Modified K-Means Clustering

Semantic grouping of the shots in a video can bethought of as first step towards scene detection. It also facilitates the easy identification of visually similar scenes. Such grouping also help in the creation of semantic content table and efficient content browsing. In this work, we present an effective scheme to form such groupings. We address the important issue of representing a shot with the help of keyframes and other sampled frames from the shot. Finally, the content of the shot is denoted by the low-level feature vectors corresponding to the representative frames. Similar shots are grouped following a modified k-means clustering algorithm. Modifications have been incorporated to accommodate the differing roles played by the keyframes and sampled frames. We have carried out the experiment with different type of video data and result obtained is satisfactory.