Semantic Analysis in Soccer Videos Using Support Vector Machine

2019 ◽  
Vol 34 (09) ◽  
pp. 2055018
Author(s):  
Mohamed M. Elgamml ◽  
Fazly S. Abas ◽  
H. Ann Goh
Keyword(s):  
Support Vector Machine ◽  
Semantic Processing ◽  
Semantic Analysis ◽  
Support Vector ◽  
Semantic Level ◽  
Kernel Support Vector Machine ◽  
Vector Machines ◽  
Kernel Support Vector Machines ◽  
The Individual ◽  
Audio Video

A tremendous increase in the video content uploaded on the internet has made it necessary for auto-recognition of videos in order to analyze, moderate or categorize certain content that can be accessed easily later on. Video analysis requires the study of proficient methodologies at the semantic level in order to address the issues such as occlusions, changes in illumination, noise, etc. This paper is aimed at the analysis of the soccer videos and semantic processing as an application in the video semantic analysis field. This study proposes a framework for automatically generating and annotating the highlights from a soccer video. The proposed framework identifies the interesting clips containing possible scenes of interest, such as goals, penalty kicks, etc. by parsing and processing the audio/video components. The framework analyzes, separates and annotates the individual scenes inside the video clips and saves using kernel support vector machine. The results show that semantic analysis of videos using kernel support vector machines is a reliable method to separate and annotate events of interest in a soccer game.

Support Vector Machine for Recognition of White Blood Cells of Leukaemia

2011 ◽  
pp. 93-122 ◽  
Author(s):  
Stanislaw Osowski ◽  
Tomasz Markiewicz
Keyword(s):  
Support Vector Machines ◽  
Blood Cells ◽  
White Blood Cells ◽  
Myelogenous Leukaemia ◽  
Image Feature ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Vector Machines ◽  
Kernel Support Vector Machines ◽  
The Individual

This chapter presents an automatic system for white blood cell recognition in myelogenous leukaemia on the basis of the image of a bone-marrow smear. It addresses the following fundamental problems of this task: the extraction of the individual cell image of the smear, generation of different features of the cell, selection of the best features, and final recognition using an efficient classifier network based on support vector machines. The chapter proposes the complete system solving all these problems, beginning from cell extraction using the watershed algorithm; the generation of different features based on texture, geometry, morphology, and the statistical description of the intensity of the image; feature selection using linear support vector machines; and finally classification by applying Gaussian kernel support vector machines. The results of numerical experiments on the recognition of up to 17 classes of blood cells of myelogenous leukaemia have shown that the proposed system is quite accurate and may find practical application in hospitals in the diagnosis of patients suffering from leukaemia.

scholarly journals Support Vector Machines to Identify Information towards Fixed-Dimensional Vector Space

2019 ◽  
Vol 8 (10) ◽  
pp. 4452-4455 ◽  
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machines ◽  
Vector Space ◽  
Support Vector ◽  
Dimensional Vector ◽  
Dimensional Vector Space ◽  
Vector Machines ◽  
Fundamental Understanding ◽  
Real World Learning ◽  
The Individual

Support vector machines have actually consulted with significant success in various real-world learning jobs. The Support Vector Machine (SVM) is a thoroughly utilized classifier. Along with yet, obtaining the finest outcomes along with SVMs needs an understanding of their procedures as well as the different implies a consumer can influence their preciseness. We supply the individual with a fundamental understanding of the concept behind SVMs and also concentrate on their usage in technique. This paper is concentrated on the useful concerns being used to support vector machines to identify information that is currently supplied as functions in some fixeddimensional vector space.

Fog Enabled Cloud Based Intelligent Resource Management Approach Using Improved Grey Wolf Optimization Strategy and Kernel Support Vector Machine

2021 ◽  
Vol 18 (4) ◽  
pp. 1275-1281
Author(s):  
R. Sudha ◽  
G. Indirani ◽  
S. Selvamuthukumaran
Keyword(s):  
Support Vector Machine ◽  
Resource Management ◽  
Support Vector ◽  
Management Approach ◽  
Optimization Strategy ◽  
Grey Wolf ◽  
Processing Load ◽  
Grey Wolf Optimization ◽  
Kernel Support Vector Machine ◽  
Service Response Time

Resource management is a significant task of scheduling and allocating resources to applications to meet the required Quality of Service (QoS) limitations by the minimization of overhead with an effective resource utilization. This paper presents a Fog-enabled Cloud computing resource management model for smart homes by the Improved Grey Wolf Optimization Strategy. Besides, Kernel Support Vector Machine (KSVM) model is applied for series forecasting of time and also of processing load of a distributed server and determine the proper resources which should be allocated for the optimization of the service response time. The presented IGWO-KSVM model has been simulated under several aspects and the outcome exhibited the outstanding performance of the presented model.

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