The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Key-frame-extraction has been recognized the important research issue in the content based on video retrieval. And the effectiveness of the key frames will directly influence on video retrieval. This paper proposes a new method of video key-frame-extraction based on block local features and mean shift clustering. Firstly, we partition the image by the block-weighted strategy, and then extract the...
This paper describes a system for automatically extracting meta-information on people from videos on the Web. The system contains multiple modules which automatically track people, including both faces and bodies, and clusters the people into distinct groups. We present new technology and significantly modify existing algorithms for body-detection, shot-detection and grouping, tracking, and track-clustering...
Video scenes provide semantic meanings for video content description and summarization. This paper explores the pair-wise visual cues of near-duplicate objects for link-constraint affinity-propagation without using keyframes. Experiments demonstrate that our method is more capable to identify scenes comparing with non-constrained clustering algorithms.
Video key-frame extraction using unsupervised clustering is an effective method to get key-frame from video clips. When multi-features are used to cluster frames, different features usually have different weight and importance. This paper introduces a feature weight based clustering method which detects the optimize cluster number and performs clustering at the same time. Starting with an over-specified...
This paper focuses on a specific type of unedited video content, called rushes, which are used for movie editing and usually present a high-level of redundancy. Our goal is to automatically extract a summarized preview, where redundant material is diminished without discarding any important event. To achieve this, rushes content has been first analysed and modeled. Then different clustering techniques...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.