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ITS » Master Theses » 51200-Teknik Informatika S2
Posted by at 17/06/2015 18:41:21  •  1504 Views


EKSTRAKSI CIRI TEKSTUR MENGGUNAKAN IMPROVED COMPLETED ROBUST LOCAL BINARY PATTERN UNTUK KLASIFIKASI CITRA BATIK

TEXTURE FEATURE EXTRACTION USING IMPROVED COMPLETED ROBUST LOCAL BINARY PATTERN FOR BATIK IMAGE CLASSIFICATION

Author :
KURNIAWARDHANI, ARRIE ( 5112201056 )




ABSTRAK

Untuk membantu proses pendokumentasian citra batik dibutuhkan sistem klasifikasi yang cukup handal dalam mengklasifikasi dan mengidentifikasi citra batik. Salah satu bagian penting dari sistem klasifikasi adalah metode ekstraksi ciri. Pemilihan metode ekstraksi ciri yang tepat sangat dibutuhkan agar dapat mencapai akurasi yang tinggi pada sistem klasifikasi. Metode ekstraksi ciri tekstur menjadi pilihan pada sistem klasifikasi kali ini karena batik direpresentasikan berdasarkan motif dasarnya. Salah satu metode ekstraksi ciri tekstur yang handal adalah Local Binary Pattern LBP. LBP adalah metode yang sederhana namun efisien dalam merepresentasikan ciri serta gray-scale invariant. Beberapa penelitian telah diajukan untuk meningkatkan kinerja LBP. Salah satunya adalah Completed Robust Local Binary Pattern CRLBP diusulkan oleh Zhao untuk mengatasi kelemahan CLBP yang sensitif terhadap noise. Namun CRLBP tidak invariant terhadap rotasi. Dari permasalahan tersebut penelitian kali ini mengusulkan pendekatan baru dari CRLBP dengan cara menyisipkan metode LBPROT ke dalam algoritma CRLBP. LBPROT adalah salah satu metode yang diusulkan untuk memperbaiki kelemahan LBP agar invariant terhadap rotasi. Pendekatan yang disebut di atas dinamakan Improved Completed Robust Local Binary Pattern ICRLBP. ICRLBP memiliki metode dasar yang sama dengan CRLBP. ICRLB memiliki 3 histogram ciri yaitu ICRLBP_Sign ICRLBP_Magnitude dan ICRLBP_Center. Algoritma LBPROT disisipkan setelah sign vector dan magnitude vector diperoleh. LBPROT mencari kombinasi nilai biner yang terkecil dari nilai biner sign vector dan magnitude vector pada setiap piksel. Kombinasi nilai biner terkecil tersebut dikonversi ke bilangan desimal. Dari nilai desimal tersebut histogram ciri ICRLBP disusun. Selanjutnya Histogram ciri ICRLBP menjadi data masukkan ke klasifikasi Probabilistic Neural Network. Kinerja sistem diukur menggunakan akurasi. Hasil uji coba menunjukkan bahwa ICRLBP dapat meningkatkan akurasi sebesar 1739 dan lebih cepat 300 kali lipat dari CRLBP pada dataset Batik. Hal ini menunjukkan bahwa ICRLBP lebih handal dibandingkan CRLBP.


ABSTRACT

Assisting the process of batik image documentation a reliable classification system is needed. One important part in the classification system is the feature extraction method. Selecting an appropriate feature extraction method is an urgent issue in order to achieve high accuracy in the classification system. Texture feature extraction method is choosen at this study because batik can be represented by its basic pattern or motif. One of reliable texture feature extraction methods is Local Binary Pattern LBP. LBP is a simple but efficient method and gray-scale invariant namely it is not affected at uneven illumination issue on the image because LBP describes texture locally. Some studies have been proposed to improve the performance of LBP such as Completed Robust Local Binary Pattern CRLBP. CRLBP is proposed by Zhao to overcome the weaknesses of CLBP that sensitive to noise. However CRLBP is not invariant to rotation. From that problem in this study a new approach of CRLBP is proposed. CRLBP algorithm will be inserted by LBPROT algorithm. LBPROT is one of improved LBP methods that proposed to overcome the LBP weakness which is not rotation invariant. The approach is called Improved Completed Robust Local Binary Pattern ICRLBP. ICRLBP has the same basic method to CRLBP. ICRLBP has three feature histograms namely ICRLBP_Sign ICRLBP_Magnitude and ICRLBP_Center. After sign vector and magnitude vector is gotten LBPROT algorithm is inserted. LBPROT looks for the smallest binary combination value of sign binary vector and magnitude binary vector in each piksel. Futhermore the smallest binary combination value is converted to decimal. That decimal value is used to build the ICRLBP histograms. ICRLBP histograms as input data is fed into classification system using Probabilistic Neural Network. The performance of classification system is evaluated using accuracy. The result experiments show that the accuracy and the speed of ICRLBP increased by 17.39 and 300 times for Batik datasets respectively. It show that ICRLBP is proven can improve the performance of CRLBP.



KeywordsBatik; Klasifikasi; Ekstraksi ciri tekstur; Completed robust local binary pattern; Rotation invariant; Probabilistic neural
 
Subject:  Komputer, Algoritma; Pengolahan data elektronis
Contributor
  1. Dr.Eng. Nanik Suciati, S.Kom., M.Kom.
  2. Isye Arieshanti, S.Kom, M.Phil.
Date Create: 17/06/2015
Type: Text
Format: PDF
Language: Indonesian
Identifier: ITS-Master-51103150001395
Collection ID: 51103150001395
Call Number: RTIf 006.42 Kur e


Source
Master Theses of Informatics Engineering, RTIf 006.42 Kur e, 2014

Coverage
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Copyright @2015 by ITS Library. This publication is protected by copyright and per obtained from the ITS Library prior to any prohibited reproduction, storage in a re transmission in any form or by any means, electronic, mechanical, photocopying, reco For information regarding permission(s), write to ITS Library




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  1.  ITS-Master-37985-5112201056-abstract_id.pdf - 166 KB
  2.  ITS-Master-37985-5112201056-abstract_en.pdf - 164 KB
  3.  ITS-Master-37985-5112201056-conclusion.pdf - 165 KB




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