scholarly journals Support-vector-machine tree-based domain knowledge learning toward automated sports video classification

2010 ◽  
Vol 49 (12) ◽  
pp. 127003
Author(s):  
Yang Jiang
Keyword(s):  
Support Vector Machine ◽  
Domain Knowledge ◽  
Support Vector ◽  
Video Classification ◽  
Sports Video ◽  
Knowledge Learning

scholarly journals Automatic Detection of Voltage Notches using Support Vector Machine

2021 ◽  
Vol 19 ◽  
pp. 528-533
Author(s):  
Rongzhen Qi ◽  
◽  
Olga Zyabkina ◽  
Daniel Agudelo Martinez ◽  
Jan Meyer
Keyword(s):  
Support Vector Machine ◽  
Domain Knowledge ◽  
Detection Efficiency ◽  
Support Vector ◽  
Svm Classifier ◽  
Automatic Method ◽  
Power Quality Disturbances ◽  
Characteristic Features ◽  
Comprehensive Framework ◽  
Nonlinear Support

This paper presents a comprehensive framework for voltage notch analysis and an automatic method for notch detection using a nonlinear support vector machine (SVM) classifier. A comprehensive simulation of the notch disturbance has been conducted to generate a diverse database. Based on domain knowledge and properties of power quality disturbances (PQDs), a set of characteristic features is extracted. After feature extraction, a set of most descriptive features has been selected with decision tree (DT) algorithm, and a nonlinear SVM classifier has been trained. Finally, the detection efficiency of the trained model is presented and discussed.

Leveraging Incrementally Enriched Domain Knowledge to Enhance Service Categorization

2012 ◽  
Vol 9 (3) ◽  
pp. 43-66 ◽  
Author(s):  
Jia Zhang ◽  
Jian Wang ◽  
Patrick Hung ◽  
Zheng Li ◽  
Neng Zhang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Structural Feature ◽  
Text Classification ◽  
Domain Knowledge ◽  
Support Vector ◽  
Iterative Approach ◽  
Classification Technique ◽  
Unique Structural Feature ◽  
Service Oriented ◽  
Open Service

This paper reports the authors’ study over an open service and mashup repository, ProgrammableWeb, which groups stored services into predefined categories. Leveraging such a unique structural feature and hidden domain knowledge of the service repository, they extend the Support Vector Machine (SVM)-based text classification technique to enhance service-oriented categorization. An iterative approach is presented to automatically verify and adjust service categorization, which will incrementally enrich domain ontology and in turn enhance the accuracy of service categorization.

scholarly journals Enhanced Video Classification System Using a Block-Based Motion Vector

Information ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 499
Author(s):  
Jayasree K ◽  
Sumam Mary Idicula
Keyword(s):  
Support Vector Machine ◽  
Classification System ◽  
Boundary Detection ◽  
Video Data ◽  
Shot Boundary Detection ◽  
Support Vector ◽  
Video Classification ◽  
Video Clips ◽  
Shot Boundary ◽  
Block Based

The main objective of this work was to design and implement a support vector machine-based classification system to classify video data into predefined classes. Video data has to be structured and indexed for any video classification methodology. Video structure analysis involves shot boundary detection and keyframe extraction. Shot boundary detection is performed using a two-pass block-based adaptive threshold method. The seek spread strategy is used for keyframe extraction. In most of the video classification methods, selection of features is important. The selected features contribute to the efficiency of the classification system. It is very hard to find out which combination of features is most effective. Feature selection makes relevance to the proposed system. Herein, a support vector machine-based classifier was considered for the classification of video clips. The performance of the proposed system considered six categories of video clips: cartoons, commercials, cricket, football, tennis, and news. When shot level features and keyframe features, along with motion vectors, were used, 86% correct classification was achieved, which was comparable with the existing methods. The research concentrated on feature extraction where combination of selected features was given to a classifier to get the best classification performance.

scholarly journals An Automatic Classification Method of Sports Teaching Video Using Support Vector Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhang Min-qing ◽  
Li Wen-ping
Keyword(s):  
Support Vector Machine ◽  
Video Segmentation ◽  
Prediction Method ◽  
Classification Performance ◽  
Video Data ◽  
Support Vector ◽  
Classification Algorithms ◽  
Video Classification ◽  
Video Feature ◽  
Different Types

There are many different types of sports training films, and categorizing them can be difficult. As a result, this research introduces an autonomous video content classification system that makes managing large amounts of video data easier. This research provides a video feature extraction approach using a support vector machine (SVM) video classification algorithm and a mix of video and audio dual-mode characteristics. It automates the classification of cartoons, ads, music, news, and sports videos, as well as the detection of terrorist and violent moments in films. To begin, a new feature expression scheme, the MPEG-7 visual descriptor subcombination, is proposed based on an analysis of the existing video classification algorithms, with the goal of addressing the problems in these algorithms. This is accomplished by analyzing the visual differences of the five video classification algorithms. The model was able to extract 9 descriptors from the four characteristics of color, texture, shape, and motion, resulting in a new overall visual feature with good results. The results suggest that the algorithm optimizes video segmentation by highlighting disparities in feature selection between different categories of films. Second, the support vector machine’s multivideo classification performance is improved by the enhanced secondary prediction method. Finally, a comparison experiment with current related similar algorithms was conducted. The suggested method outperformed the competition in the accuracy of video classification in five different types of videos, as well as in the recognition of terrorist and violent incidents.

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