By Ziyou Xiong, Regunathan Radhakrishnan, Ajay Divakaran, Yong Rui, Thomas S. Huang
Huge volumes of video content material can purely be simply accessed by means of swift looking and retrieval innovations. developing a video desk of contents (ToC) and video highlights to permit finish clients to sift via all this information and locate what they wish, once they wish are crucial. This reference places forth a unified framework to combine those services helping effective searching and retrieval of video content material. The authors have built a cohesive strategy to create a video desk of contents, video highlights, and video indices that serve to streamline using functions in client and surveillance video purposes.
The authors talk about the iteration of desk of contents, extraction of highlights, assorted thoughts for audio and video marker popularity, and indexing with low-level gains resembling colour, texture, and form. present functions together with this summarization and skimming know-how also are reviewed. functions comparable to occasion detection in elevator surveillance, spotlight extraction from activities video, and snapshot and video database administration are thought of in the proposed framework. This ebook offers the most recent in learn and readers will locate their look for wisdom pleased through the breadth of the data lined during this quantity.
* bargains the most recent in innovative examine and functions in surveillance and buyer video
* Presentation of a singular unified framework geared toward effectively sifting throughout the abundance of pictures collected day-by-day at purchasing department stores, airports, and different advertisement facilities
* Concisely written via major participants within the sign processing with step by step guideline in development video ToC and indices
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Additional resources for A Unified Framework for Video Summarization, Browsing and Retrieval. With Applications to Consumer and Surveillance Video
This discontinuity may cause the clustering to be wrong and make the clustering results sensitive to the window size. 18). Temporal attraction effectively models the importance of time locality and does not cause any discontinuity in grouping. • Direct merge to a scene. 3 The Proposed Approach 25 merged to the scene. However, it may be similar to a certain degree to most of the groups in a scene. For example, a camera shoots three sets of shots of a person from three angles: 30°, 60°, and 45°. Obviously, the three sets of shots will form three groups.
This number is usually chosen through cross-validation. The practical problem is that for some class, this number will lead to overfitting of the training data if it is much less than the actual one, or, inversely, underfitting of the data. Our solution is to use the minimum description length (MDL) criterion in selecting the number of mixtures. MDL-GMMs fit the training data to the generative process as closely as possible, avoiding the problem of overfitting or underfitting. 1 ESTIMATING THE NUMBER OF MIXTURES IN GMMs Theoretical Derivations The derivations here follow those in Bouman .
Particular portion of video, without resorting to the tedious fast-forward and rewind functions. Imagine the situation if we do not have a ToC for a long book. It may take hours to grasp the main content. The same is true for videos. The scene-based video ToC just described greatly facilitates the user's access to the video. It not only provides the user with nonlinear access to the video (in contrast to conventional linear fast-forward and rewind), but it also gives the user a global picture of the whole story.