Storage Retrieval

Download A Unified Framework for Video Summarization, Browsing and by Ziyou Xiong, Regunathan Radhakrishnan, Ajay Divakaran, Yong PDF

By Ziyou Xiong, Regunathan Radhakrishnan, Ajay Divakaran, Yong Rui, Thomas S. Huang

Huge volumes of video content material can in simple terms be simply accessed by means of swift looking and retrieval thoughts. developing a video desk of contents (ToC) and video highlights to let finish clients to sift via all this information and locate what they need, after they wish are crucial. This reference places forth a unified framework to combine those features assisting effective searching and retrieval of video content material. The authors have constructed a cohesive solution to create a video desk of contents, video highlights, and video indices that serve to streamline using functions in shopper and surveillance video functions.

The authors speak about the new release of desk of contents, extraction of highlights, various concepts for audio and video marker acceptance, and indexing with low-level positive factors reminiscent of colour, texture, and form. present purposes together with this summarization and skimming know-how also are reviewed. purposes comparable to occasion detection in elevator surveillance, spotlight extraction from activities video, and picture and video database administration are thought of in the proposed framework. This publication offers the most recent in learn and readers will locate their look for wisdom completely happy via the breadth of the data coated during this quantity.

* bargains the newest in innovative examine and purposes in surveillance and patron video

* Presentation of a unique unified framework aimed toward effectively sifting during the abundance of photos accumulated day-by-day at buying department shops, airports, and different advertisement facilities

* Concisely written through best members within the sign processing with step by step guideline in development video ToC and indices

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Extra info for A Unified Framework for Video Summarization, Browsing and Retrieval. With Applications to Consumer and Surveillance Video

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Let us denote the parameter learning of GMMs using the MDL criterion MDL-GMM. While the expectation maximization (EM) algorithm can be used to update the parameter 6, it does not solve the problem of how to change the model order K. Our approach will be to start with a large number of clusters and then sequentially decrement the value of K. For each value of K, we will apply the EM update until we converge to a local minimum of the MDL functional. After we have done this for each value of ^ , we may simply select the value of K and corresponding parameters that resulted in the smallest value of the MDL criterion.

9. The cameras are usually positioned in the center of the two sides of the field. The camera operators pan the camera in order to go back and forth between two halves of the field and zoom to focus on special targets. Since the distance between the camera and either of the two goalposts is relatively much larger than the size of the goalpost itself, little change occurs in the pose of the goalpost during the entire game, irrespective of the camera pan or zoom. 9. 11. Robust identification of those video frames containing either of the two goalposts can bring us to the vicinity of soccer highlights.

We disregard the V component because it is less robust to the lighting condition. At the key frame level, visual features are extracted to characterize the spatial information. 4) where bi and et are the beginning and ending frames of shot i. 5) which captures both the spatial and the temporal information of a shot. At higher levels, this spatial-temporal information is used in grouping and scene structure construction. 3 TIME-ADAPTIVE GROUPING Before we construct the scene structure, it is convenient to first create an intermediate entity group to facilitate the process.

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