Implementation of high performance objectionable video classification system

2006 ◽  
Author(s):  
Hogyun Lee ◽  
Seungmin Lee ◽  
Taekyong Nam
Keyword(s):  
Classification System ◽  
High Performance ◽  
Video Classification

scholarly journals Deep Learning Approaches to Automated Video Classification of Upper Limb Tension Test

Healthcare ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1579
Author(s):  
Wansuk Choi ◽  
Seoyoon Heo
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
High Performance ◽  
Model Fit ◽  
Fine Tuning ◽  
Learning Approaches ◽  
Video Classification ◽  
Video Clips ◽  
Performance Computing

The purpose of this study was to classify ULTT videos through transfer learning with pre-trained deep learning models and compare the performance of the models. We conducted transfer learning by combining a pre-trained convolution neural network (CNN) model into a Python-produced deep learning process. Videos were processed on YouTube and 103,116 frames converted from video clips were analyzed. In the modeling implementation, the process of importing the required modules, performing the necessary data preprocessing for training, defining the model, compiling, model creation, and model fit were applied in sequence. Comparative models were Xception, InceptionV3, DenseNet201, NASNetMobile, DenseNet121, VGG16, VGG19, and ResNet101, and fine tuning was performed. They were trained in a high-performance computing environment, and validation and loss were measured as comparative indicators of performance. Relatively low validation loss and high validation accuracy were obtained from Xception, InceptionV3, and DenseNet201 models, which is evaluated as an excellent model compared with other models. On the other hand, from VGG16, VGG19, and ResNet101, relatively high validation loss and low validation accuracy were obtained compared with other models. There was a narrow range of difference between the validation accuracy and the validation loss of the Xception, InceptionV3, and DensNet201 models. This study suggests that training applied with transfer learning can classify ULTT videos, and that there is a difference in performance between models.

An Embedded GPU Accelerated Hyperspectral Video Classification System in Real-Time

2021 ◽  
Author(s):  
Jaime Sancho ◽  
Manuel Villa ◽  
Gemma Urbanos ◽  
Marta Villanueva ◽  
Pallab Sutradhar ◽  
...  
Keyword(s):  
Real Time ◽  
Classification System ◽  
Video Classification ◽  
Embedded Gpu

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.

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