A new approach for video indexing and retrieval based on visual features

This work is concerned with video indexing and retrieval based on visual features. It puts forward an approach for the automatic summary and indexing of digital videos in order to support queries based on visual content on a video database. The proposed approach was applied to a video collection containing more than 34 hours of broadcast news videos. Visual features extracted from the summarized version of the videos were then used for video content indexing. That provided us with the basis for various experiments and analyses on the retrieval of visual content with the application of various techniques implemented. The approach proposes a method for key frame extraction that summarizes video content in a static storyboard, specifically designed for key frame retrieval and video access. Thus, the selected key frames are processed in order to extract statistical features as well as wavelet coefficients to represent the video’s essence in a very short amount of data while preserving its main content characteristics.