Video summarization is a method to generate succinct version of a video by eliminating the redundant frames. The representation of video summaries using key frames is a simple and effective way to generate video summaries. However, eliciting the frames that effectively characterize a video is a daunting task. A popular way to extract key frames is to compute the frame difference between the con...

The problem we are considering here is duplicate video detection. We have a database of N videos and we store compact signatures, called fingerprints, for each of them. When a query video is presented, the system first returns the top-K most closely matched videos. Then, a more detailed search is performed among the top-K retrieved model videos to obtain the best match. Finally, a separate modu...

This paper presents a system designed for the management of multimedia databases that embarks upon the problem of efficient media processing and representation for automatic semantic classification and modelling. Its objectives are founded on the integration of a large-scale wildlife digital media archive with a manually annotated semantic metadata organised in a structured taxonomy and media c...

A convenient and most effective method of querying a video database for robust face recognition is by using keyframes extracted from the image sequence. In this paper we present a clustering based approach that bypasses the need for shot detection or segmentation, to extract the key-frames from the video using the local features, for the purpose of face recognition. Local features which are ins...

Automatic reconstruction of 3D models from video sequences requires selection of appropriate video frames for performing the reconstruction. We introduce a complete method for key frame selection that automatically avoids degeneracies and is robust to inaccurate correspondences caused by motion blur. Our method combines selection criteria based on the number of frame-to-frame point corresponden...

We investigate the application of a variety of content-based image retrieval techniques to the problem of video retrieval. We generate large numbers of features for each of the key frames selected by a highly effective shot boundary detection algorithm to facilitate a query by example type search. The retrieval performance of two learning methods, boosting and k-nearest neighbours, is compared ...

Video search results and suggested videos on web sites are represented with a video thumbnail, which is manually selected by the video up-loader among three randomly generated ones (e.g., YouTube). In contrast, we present a grounded user-based approach for automatically detecting interesting key-frames within a video through aggregated users’ replay interactions with the video player. Previous ...

The insufficiency of labeled training data for representing the distribution of the entire dataset is a major obstacle in automatic semantic annotation of large-scale video database the objective is to represent the most “important” or “meaningful” scenes of the large amount of visual information by only a few images:the key frames. First, the image sequences are temporally segmented into conti...

In order to identify videos on the Internet, a robust video hashing based on key frames and Isometric Feature Mapping (ISOMAP) is proposed in this paper. In this method, key frames extraction and hash generation are two major components. During the video hashing, key frame extraction is achieved by a uniform distribution vector and video tomography analysis, while hash generation is accomplishe...

The large amount of videos usage increase the volume of data, more time to access and more man power is required. Video summarization is the solution for this problem. Summarized video can be used to review the important aspect of particular video, indexing and faster browsing. Video summarization techniques are classified into key frame based classification and skim based classification. This ...