The Effect of Normalization in Violence Video Classification Performance

International Research and Innovation Summit 2017

Ali, Ashikin^1 ; Senan, Norhalina^1

Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Batu Pahat

86400, Malaysia^1

关键词: Classification performance;    Classification rates;    Data preprocessing;    Min-max normalizations;    Multi layer perceptron;    Pre-processing stages;    Problem statement;    Video classification;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/226/1/012082/pdf
DOI  :  10.1088/1757-899X/226/1/012082

来源: IOP

【 摘 要 】

Basically, data pre-processing is an important part of data mining. Normalization is a pre-processing stage for any type of problem statement, especially in video classification. Challenging problems that arises in video classification is because of the heterogeneous content, large variations in video quality and complex semantic meanings of the concepts involved. Therefore, to regularize this problem, it is thoughtful to ensure normalization or basically involvement of thorough pre-processing stage AIDS the robustness of classification performance. This process is to scale all the numeric variables into certain range to make it more meaningful for further phases in available data mining techniques. Thus, this paper attempts to examine the effect of 2 normalization techniques namely Min-max normalization and Z-score in violence video classifications towards the performance of classification rate using Multi-layer perceptron (MLP) classifier. Using Min-Max Normalization range of [0,1] the result shows almost 98% of accuracy, meanwhile Min-Max Normalization range of [-1,1] accuracy is 59% and for Z-score the accuracy is 50%.