Degree Type

Thesis

Date of Award

2019

Degree Name

Master of Science

Department

Computer Science

Major

Computer Science

First Advisor

Adisak . Sukul

Second Advisor

Wallapak . Tavanapong

Abstract

Today’s digital world consists of vast multimedia contents: images, audios and videos. Thus, the availability of huge video datasets have encouraged researchers to design video classification techniques to group videos into categories of interest. One of the topics of interest to political scientists is automated classification of a video advertisement into a political campaign ad category or others. Recent years have seen a plethora of deep learning-based methods for image and video classification. These methods learn feature representation from the training data along with the classification model. We investigate the effectiveness of three recent deep-learning based video classification techniques for the political video advertisement classification. The best technique among the three yields an accuracy of 80%. In this thesis, we further improve the classification accuracy by combining the results of classification of text features with that of the best deep learning methods we studied. Our method achieves the classification accuracy of 91%.

Copyright Owner

Aashish Dhakal

Language

en

File Format

application/pdf

File Size

53 pages

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