Plain, edge, texture (PET) block classifier using Tchebichef moments and SVM

Chern Loon Lim, Kim Han Thung, Yong Poh Yu, Siaw Lang Wong, P. Raveendran

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)

Abstract

This paper presents an image block classification method using Tchebichef moments (TMs) and support vector machine (SVM). The test images are divided into non-overlapping 16 x 16 blocks and transformed into moment domain using Discrete Tchebichef Transform. These moment features are then used in the image content (block) classification. SVM is used for learning and classifying the blocks into three types: "plain", "edge" and "texture", based on their moment energy level. Experimental results show that the proposed method works well and the classification accuracy is 98.7%.

Original languageEnglish
Title of host publicationISPACS 2013 - 2013 International Symposium on Intelligent Signal Processing and Communication Systems
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages409-412
Number of pages4
ISBN (Print)9781467363617
DOIs
Publication statusPublished - 2013
EventIEEE International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS) 2013 - Naha, Okinawa, Japan
Duration: 12 Nov 201315 Nov 2013
https://ieeexplore.ieee.org/xpl/conhome/6691895/proceeding (Proceedings)

Conference

ConferenceIEEE International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS) 2013
Abbreviated titleISPACS 2013
Country/TerritoryJapan
CityNaha, Okinawa
Period12/11/1315/11/13
Internet address

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