Classification of MPEG Video Content Using Divergence Measure with Data Covariance

2005 ◽  
pp. 969-980
Author(s):  
Dong-Chul Park ◽  
Chung-Nguyen Tran ◽  
Yunsik Lee
Keyword(s):  
Divergence Measure ◽  
Video Content

scholarly journals Watching a Small Portion could be as Good as Watching All: Towards Efficient Video Classification

2018 ◽  
Author(s):  
Hehe Fan ◽  
Zhongwen Xu ◽  
Linchao Zhu ◽  
Chenggang Yan ◽  
Jianjun Ge ◽  
...  
Keyword(s):  
Large Scale ◽  
Sampling Rate ◽  
Computational Cost ◽  
Confidence Score ◽  
Video Content ◽  
Video Classification ◽  
Video Frames ◽  
Similar Accuracy ◽  
Efficient Video

We aim to significantly reduce the computational cost for classification of temporally untrimmed videos while retaining similar accuracy. Existing video classification methods sample frames with a predefined frequency over entire video. Differently, we propose an end-to-end deep reinforcement approach which enables an agent to classify videos by watching a very small portion of frames like what we do. We make two main contributions. First, information is not equally distributed in video frames along time. An agent needs to watch more carefully when a clip is informative and skip the frames if they are redundant or irrelevant. The proposed approach enables the agent to adapt sampling rate to video content and skip most of the frames without the loss of information. Second, in order to have a confident decision, the number of frames that should be watched by an agent varies greatly from one video to another. We incorporate an adaptive stop network to measure confidence score and generate timely trigger to stop the agent watching videos, which improves efficiency without loss of accuracy. Our approach reduces the computational cost significantly for the large-scale YouTube-8M dataset, while the accuracy remains the same.

scholarly journals Own Video Content of Libraries: Trends and Prospects for Use

2020 ◽  
Vol 69 (3) ◽  
pp. 261-270
Author(s):  
Nadezhda V. Popova
Keyword(s):  
Positive Impact ◽  
Video Content ◽  
Media Environment ◽  
Current Trends ◽  
Internet Users ◽  
Wide Range ◽  
The Media ◽  
Goals And Objectives

Changing of media consumption in the digital age is the object of focus attention of both Russian and foreign researchers. Modern cultural studies note the increasing role of video content in the media environment. Taking into account current trends, libraries more often create their own video materials to implement various goals and objectives. Thus, creation of own video content is rapidly becoming an integral part of the work of modern library. However, despite some established experience of libraries in this area, there is still not enough research of general and theoretical nature on the content, guidelines and prospects for the development of activities related to the creation and use of library video content, and there is no its classification.The purpose of this work is to conduct analysis of the video content in libraries, identify the most common materials, as well as to determine the prospects for using this tool to reach their own goals. The article discusses definitions of the term “video content”. The author presents the main types of videos produced by libraries, their characteristics and features. Special attention is paid to video projects of libraries in Russia. The article reveals the experience of the Astrakhan Library for Youth named after B. Shakhovsky in using its own video content. The paper discusses the issue of classification of video materials produced by libraries. The author proposes the following classification of library videos: video review, virtual book exhibition, webinar (online seminar or web conference), interview, humorous video, webcast, event announcement, video report and booktrailer. The author indicates the main reasons hindering the demand for library video content among the wide range of Internet users and gives the rationale for the necessity and importance of this type of activity and proposes possible prospects for using own video content of libraries. Thanks to its presence, the library ceases to be a closed institution storing knowledge within itself that produces positive impact on its image. Using means of communication that are understandable to a person of visual culture, it changes stereotypes and demonstrates its modern capabilities.

scholarly journals CLDC: Efficient Classification of Medical Data Using Class Level Disease Convergence Divergence Measure

2019 ◽  
Vol 8 (10) ◽  
pp. 2256-2262
Keyword(s):  
Classification Performance ◽  
Medical Data ◽  
Divergence Measure ◽  
Data Set ◽  
It Convergence ◽  
Class Level ◽  
Data Points ◽  
Medical Data Classification ◽  
Different Dimensions

The problem of medical data classification is analyzed and the methods of classification are reviewed in various aspects. However, the efficiency of classification algorithms is still under question. With the motivation to leverage the classification performance, a Class Level disease Convergence and Divergence (CLDC) measure based algorithm is presented in this paper. For any dimension of medical data, it convergence or divergence indicates the support for the disease class. Initially, the data set has been preprocessed to remove the noisy data points. Further, the method estimates disease convergence/divergence measure on different dimensions. The convergence measure is computed based on the frequency of dimensional match where the divergence is estimated based on the dimensional match of other classes. Based on the measures a disease support factor is estimated. The value of disease support has been used to classify the data point and improves the classification performance.

Research on the Application of Multimedia Simulation Technology

2013 ◽  
Vol 846-847 ◽  
pp. 1780-1783
Author(s):  
Meng Liu ◽  
Sheng Dong Yang ◽  
Yang Wang
Keyword(s):  
Recognition Rate ◽  
Difficult Problem ◽  
Video Content ◽  
Video Capture ◽  
Color Information ◽  
Computer Data ◽  
The Real ◽  
Real Process ◽  
Information Semantics

The multimedia technology has been widely applied to many engineering fields. However, because the data contained in video content is very large, it is always being a difficult problem of computer data analysis and processing to analyze the video. Based on the content analysis, this paper takes use of many technologies aimed at the problem of video, such as analysis and processing of multimedia, simulation classification of computer and computer vision and so on. At the same time, combined with the model of color information semantics and the real target tracking principle, this paper builds model and designs the algorithm for the video simulation. At last, this paper makes trajectory extraction and recognition for the real process goals of football, establishing the simulation process of football. Through the numerical simulation, it is found that frames extracted from the video capture are different from each other in the process of real football game and the recognition rate and accuracy of simulation trajectory are also not the same. Among them, when frame is 85, the effects of recognition rate and accuracy are best, which respectively reach 80% and 89%. Thus, it gains a better simulation effect.

scholarly journals Classification of Tennis Video Types Based on Machine Learning Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xun Gong ◽  
Fucheng Wang
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Rapid Development ◽  
Video Data ◽  
Learning Technology ◽  
Video Content ◽  
Video Classification ◽  
The World ◽  
Spatial Detection

With the rapid development of online video data, how to find the required information has become an urgent problem to be solved. This article focuses on sports videos and studies video classification and content-based retrieval techniques. Its purpose is to establish a mark and index of video content and to promote user acquisition through computer processing, analysis, and understanding of video content. Video tennis classification has high research and application value. This article focuses on video tennis based on the selection of the basic frame of each shot and proposes an algorithm for classification of shots based on average grouping. Based on this, we use a color-coded spatial detection method to detect the type of tennis match. Then, it integrates the results of audiovisual analysis to identify and classify exciting events in tennis matches. According to statistics, although the number of people participating in tennis cannot enter the top ten, the number of spectators ranks fourth. Four tennis tournaments, masters, and crown tournaments are held every year around the world. Watching large-scale international tennis matches has become a pillar of leisure and vacation for many people. Tennis matches last from two hours to four hours or more, and there are countless large and small tennis matches around the world every year, so the number of tennis records created is staggering. And artificial intelligence technology is rarely used in tennis in the sports world (5%), but football has reached 50%. Therefore, when dealing with such a large amount of data, we urgently need to find a fast and effective video retrieval classification method to find the required information. The experiment of tennis video classification research based on machine learning technology proves that the accuracy of tennis video classification reaches 98%, so this system has high feasibility.

Note on the spectral classification of carbon stars in the photographic infrared region

1966 ◽  
Vol 24 ◽  
pp. 21-23
Author(s):  
Y. Fujita
Keyword(s):  
Infrared Region ◽  
Spectral Classification ◽  
Carbon Stars

We have investigated the spectrograms (dispersion: 8Å/mm) in the photographic infrared region fromλ7500 toλ9000 of some carbon stars obtained by the coudé spectrograph of the 74-inch reflector attached to the Okayama Astrophysical Observatory. The names of the stars investigated are listed in Table 1.

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