Unsupervised Classification of Videos Using Semantic Analysis (PhD Thesis) (Record no. 363950)

MARC details
000 -LEADER
fixed length control field 03987nam a2200217Ia 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 200207s2019||||xx |||||||||||||| ||eng||
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
ISSN-L phd
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title English
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 004.019378242
Item number WAS
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Waseemullah,
Relator term author
245 #0 - TITLE STATEMENT
Title Unsupervised Classification of Videos Using Semantic Analysis (PhD Thesis)
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Karachi :
Name of publisher, distributor, etc. NED University of Engineering and Technology Department of Computer Science and Information Technology,
Date of publication, distribution, etc. 2019
300 ## - PHYSICAL DESCRIPTION
Extent XX, 134 p.
Other physical details : ill
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes Bibliographical References
520 ## - SUMMARY, ETC.
Summary, etc. ABSTRACT :<br/><br/>Advertisements displayed in TV broadcast are a very important part of transmission as majority of revenue for a broadcaster is generated by advertising. Fast and accurate advertisement discovery is an important issue in research community of computer vision. The main challenge for advertisement detection is lack of legislation for media industry in Pakistan that ensures the separate identification of advertisements and other non-advertisement programs through blank frame or absence of TV channel logo during TV transmission. <br/>In this thesis a framework for TV Commercial Detection and Identification (UTCDI) is proposed. The framework is composed of two sections; <br/>1.) Unsupervised detection of TV commercials. <br/>2.) Recognition or identification of commercials. <br/>An algorithm has been developed for TV commercials (ad) detection using semantic analysis. The term semantic in video processing refers to the any high-level concept that human can understand such as soccer ball in sports video, lion in animal planet documentary program, a ship and a rock. In case of this research study, an ad is semantic feature, which is composed of other semantic notions such as shots and scenes. The proposed algorithm uses colour histogram feature to detect scene change points in video. Furthermore, these scenes are used for unsupervised advertisement detection. Algorithm assigns unique ID automatically to each scene; boundaries of each repeating pattern of scenes are identified. If the repeating patterns are repeated more than a threshold value then they are marked as a commercial and assigned an ad ID. This approach is termed as semantic advertisement discovery. As this approach solely relies on repeated scenes which is high-level concept rather than computing text, edge and shape of any object from each frame in result the approach saves the computation and time. <br/>This approach successfully results the unsupervised discovery of TV advertisement with <br/>average value of precision 97% and average recall rate is 85%. Meanwhile, it suffers a problem that the results heavily rely on the size of video file. The more the size of video data the higher would be the chance of repeated scenes, the shorter the size of video data the lesser would be the chance of advertisement discovery as it depends on the threshold value for repeated scenes. Another factor that significantly influences the advertisement detection is quality of video. The proposed algorithm does not provide good ad detection results on grainy and poor quality videos. <br/><br/>In the second section, The SURF (Speeded Up Robust Features) feature is computed of key object from candidate advertisement. Then the SURF feature and the colour histogram feature are used to obtain a train file - which in result recognize and identify target commercial in a TV transmission file. <br/><br/>The framework for ad discovery based on semantic segmentation of broadcasted TV transmission can be used for PEMRA (Pakistan Electronic Media Regulatory Authority) to identify a particular ad and its statistics. The framework is also usable for Advertisements agency to visualize or measure the air time used for different ads. Experimental results confirm the importance of the proposed framework. <br/>
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Human Computer Interaction Thesis
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Semantic Computing Thesis
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element TV ads Thesis
9 (RLIN) 883196
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Ad Detection Thesis
9 (RLIN) 883197
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type PHD Thesis
Source of classification or shelving scheme Dewey Decimal Classification
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Withdrawn status Lost status Physical Form Damaged status Not for loan Home library Current library Shelving location Date acquired Stock Type Total Checkouts Full call number Barcode Date last seen Accession Date Koha item type
    Text, Hardcover     Government Document Section Government Document Section Govt Publication Section 20/10/2022 Donation   004.019378242 WAS 96719 20/10/2022 07/02/2020 Reference Collection
    Text, Hardcover     Government Document Section Government Document Section Govt Publication Section 20/10/2022 Donation   004.019378242 WAS 96720 20/10/2022 07/02/2020 Reference Collection