Temu Kembali Citra Menggunakan Multi Texton Co-Occurrence Descriptor

Sistem temu kembali citra masih menjadi topik penelitian yang belum terselesaikan. Beberapa metode ekstraksi fitur untuk temu kembali citra telah dikerjakan sebelumnya, diantaranya Gray Level Co-Occurrence Matrix (GLCM), Texton Co- Occurrence Histogram (TCM), Multi Texton Histogram (MTH), Micro Stucture Descriptor (MSD), Enhanced Micro Strcuture Descriptor (EMSD) dan Color difference Histogram (CDH). Namun, penelitian tersebut masih memiliki precision rata-rata 40%- 60%, sehingga masih perlu dikembangkan lebih lanjut. Dibandingkan dengan TCM, MSD, EMSD dan CDH, pendekatan menggunakan MTH memiliki kompleksitas komputasi yang lebih sederhana, sehingga untuk melakukan temu kembali citra menjadi lebih cepat. Namun demikian MTH memiliki kekurangan dalam merepresentasikan fitur. Pertama, MTH hanya menggunakan fitur lokal dalam merepresentasikan citra. Kedua, dalam pendeteksian pasangan piksel menggunakan Texton, ada informasi pasangan piksel yang terlewatkan sehingga dapat mengurangi representasi citra. Penelitian ini mengusulkan pendekatan baru untuk melakukan ekstraksi fitur pada sistem temu kembali citra. Kontribusi penelitian ini yaitu menambahkan jenis Texton baru untuk mendeteksi pasangan piksel dan menambahkan fitur GLCM. Metode yang diusulkan pada penelitian ini dinamakan Multi Texton Co-Occurrence Descriptor (MTCD). MTCD melakukan ekstraksi fitur warna, tekstur dan bentuk secara simultan menggunakan Texton, kemudian menghitung representasi citra secara global dengan GLCM. Texton mendeteksi konkurensi pasangan pixel pada setiap komponen RGB dan orientasi tepi citra, sedangkan GLCM merepresentasikan citra dengan sudut pandang global yang dihasilkan dari energy, entropy, contrast dan correlation. Fitur akhir MTCD berupa histogram hasil dari deteksi Texton dan GLCM. Data yang digunakan untuk temu kembali citra menggunakan 300 data Batik dan 10.000 data Corel. Pengukuran kemiripan citra menggunakan Canberra dan pengukuran performa MTCD menggunakan precision dan recall. Data uji dipilih secara acak terdiri dari 50 d ata Batik, 2.500 untuk data Corel 5.000 dan 5.000 untuk data Corel 10.000. Berdasarkan hasil uji coba yang telah dilakukan, penambahan 2 texton baru dan fitur GLCM dapat meningkatkan precision 2,86% pada data Batik, 3,40% pada data Corel 5.000 dan 3,06% pada data Corel 10.000. MTCD lebih unggul daripada MTH untuk temu kembali citra. ============================================================================================================================= Image retrieval system is one of a challenging topic and is not yet finalized. A number of features extraction methods has been proposed, for example Gray Level Co- Occurrence Matrix (GLCM), Texton Co-Occurrence Histogram (TCM), Multi Texton Histogram (MTH), Micro Stucture Descriptor (MSD), Enhanced Micro Structure Descriptor (EMSD) and Color difference Histogram (CDH). However, the precision rate of those methods are relatively low, between 40% and 60%. Therefore, there is a need of a new approach to improve the results. Looking to those methods, in term of computational complexity, MTH is the simplest. The problem is that there is weakness in representing image features. First, MTH using local features to representate the image. Second, The weakness occurs in the proces of detecting pairs of pixel using texton for color quatization and edge orientation quantization. This study proposes a new approach to perform features extraction in image retrieval systems. Contribution of this study is to add new types of Texton to detect pairs of pixels and adding GLCM features. The method in this study is called Multi Texton Co-Occurrence Descriptor (MTCD). MTCD works by extracting color features, texture features and shape features simultaneously using Texton, then calculates the global image representations with GLCM. Texton detects concurrency of pairs of pixels on each RGB component and the edge orientation of image, while GLCM represents the image as global viewpoint by the value of energy, entropy, contrast and correlation. Features that are detected by MTCD are presented as histogram. The data used in this study is a 300 batik data and a 10,000 Corel data. In order to measure image similarity, Canberra Distance is used. For performance measurement, precision and recall are used. Test data randomly selected consists of 50 Batik data, 2,500 for Corel 5.000 and 5.000 for Corel 10.000. Based on the results of the testing that has been done, the addition of 2 new texton and GLCM features can improve the precision 2.86%, 3 ,40% and 3,06% on Batik, Corel 5.000 and Corel 10.000 respectively. MTCD is superior than MTH for image retrieval

HII: Histogram Inverted Index For Fast Images Retrieval

This work aims to improve the speed of search by creating an indexing structure in CBIR system. We utilised an inverted index structure that usually used in text retrieval with a modification. The modified inverted index is built based on histogram data that generated using Multi Texton Histogram (MTH) and Multi Texton Co-Occurrence Descriptor (MTCD) from 10,000 images of Corel dataset. When building the inverted index, we normalised value of each feature into a real number and considered pairs of feature and value that owned by a particular number of images. Based on our investigation, on MTCD histogram of 5,000 data test, we found that by considering histogram variable values which owned by maximum 12% of images, the number of comparison for each query can be reduced by 67.47% in a rate, the precision is 82.2%, and the rate of access to disk is 32.83%. Furthermore, we named our approach as Histogram Inverted Index (HII).

CBIR of Batik Images using Micro Structure Descriptor on Android

Batik is part of a culture that has long developed and known by the people of Indonesia and the world. However, the knowledge is only on the name of batik, not at a more detailed level, such as image characteristic and batik motifs. Batik motif is very diverse, different areas have their own motifs and patterns related to local customs and values. Therefore, it is important to introduce knowledge about batik motifs and patterns effectively and efficiently. So, we build CBIR batik using Micro-Structure Descriptor (MSD) method on Android platform. The data used consisted of 300 images with 50 classes with each class consists of six images. Performance test is held in three scenarios, which the data is divided as test data and data train, with the ratio of scenario 1 is 50%: 50%, scenario 2 is 70%, 30%, and scenario 3 is 80%: 20%. The best results are generated by scenario 3 with precision valur 65.67% and recall value 65.80%, which indicates that the use of MSD on the android platform for CBIR batik performs well

Image Retrieval Based on Multi Structure Co-occurrence Descriptor

This study present a new technique for Batik cloth image retrieval using Micro-Structure Co-occurence Descriptor (MSCD). MSCD is a developed method based on Enhanced Micro Structure Descriptor (EMSD). Previously, EMSD has been improved by adding edge orientation feature. In previous study, EMSD cannot achieve an optimal precision. Therefore, MSCD is proposed to overcome the EMSD drawback using global feature approach, namely Gray Level Co-occurrence Matrix (GLCM). There are 300 batik cloth images which contain 50 classes used for dataset. The performance result show that MSCD can retrieve Batik cloth images more effective than EMSD

Vehicle Classification using Haar Cascade Classifier Method in Traffic Surveillance System

Object detection based on digital image processing on vehicles is very important for establishing monitoring system or as alternative method to collect statistic data to make efficient traffic engineering decision. A vehicle counter program based on traffic video feed for specific type of vehicle using Haar Cascade Classifier was made as the output of this research. Firstly, Haar-like feature was used to present visual shape of vehicle, and AdaBoost machine learning algorithm was also employed to make a strong classifier by combining specific classifier into a cascade filter to quickly remove background regions of an image. At the testing section, the output was tested over 8 realistic video data and achieved high accuracy. The result was set 1 as the biggest value for recall and precision, 0.986 as the average value for recall and 0.978 as the average value for precision