Hierarchical representation of video sequences for annotation

Abstract

Video annotation is an important issue in video content management systems. Rapid growth of the digital video data has created a need for efficient and reasonable mechanisms that can ease the annotation process. In this paper, we propose a novel hierarchical clustering based system for video annotation. The proposed system generates a top-down hierarchy of the video streams using hierarchical k-means clustering. A tree-based structure is produced by dividing the video recursively into sub-groups, each of which consists of similar content. Based on the visual features, each node of the tree is partitioned into its children using k-means clustering. Each sub-group is then represented by its key frame, which is selected as the closest frame to the centroids of the corresponding cluster, and then can be displayed at the higher level of the hierarchy. The experiments show that very good hierarchical view of the video sequences can be created for annotation in terms of efficiency.