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

Temu Kembali Citra Makanan Menggunakan Representasi Multi Texton Histogram

Temu kembali citra merupakan suatu metode pencarian citra dengan melakukan perbandingan antara citra query dengan citra yang terdapat dalam database berdasarkan informasi yang ada. Temu kembali citra untuk pengenalan makanan sangat dibutuhkan untuk pasien diet. Dalam tugas akhir ini diusulkan suatu metode temu kembali citra makanan berdasarkan input berupa citra makanan yang dibandingkan dengan database citra makanan yang ada. Tugas akhir ini secara khusus membahas mengenai perancangan sebuah sistem temu kembali citra makanan dengan representasi Multi-texton Histogram (MTH). Proses pertama dilakukan deteksi orientasi tekstur menggunakan metode Sobel Edge Detection. Setelah itu dilakukan kuantisasi warna pada ruang warna RGB. Serta deteksi Texton untuk tahap ekstraksi fiturnya. Untuk mendapatkan kemiripan citra, dihitung jarak antar citra dengan menggunakan distance metric. Setelah didapatkan jarak antar citra, diurutkan dari yang terdekat sampai yang terjauh jarak citranya untuk menentukan temu kembali citra. Hasil yang didapat adalah berupa ditemukannya citra-citra yang mirip dengan citra query. Berdasarkan uji coba yang dilakukan pada citra, hasil pencarian citra mirip dengan rata-rata nilai precision terbaik sebesar 40,50% dan recall terbaik sebesar 8,61% pada 18 level orientasi dan 64 level kuantisasi warna. ====================================================================== Image retrieval is an image search method by performing a comparison between the query image and the image contained in the database based on the existing information. Image retrieval for feeding recognition is essential for dietary patients. In this final project proposed a method of retrieval of food image based on input in the form of food image compared with existing food image database. This final project specifically discusses the design of a food image retrieval system with a Multi-texton Histogram (MTH) representation. The first step is detection of texture orientation using Sobel Edge Detection method. After that is done the color quantization on RGB color space. As well as Texton Detection for its feature extraction stage. To get the image resemblance, calculated the distance between the image using distance metric. Having obtained the distance between images, sorted from the nearest to the farthest distance of its image to determine image retrieval. The results obtained are in the form of finding images similar to the image of the query. Based on the experiments performed on the image, the best precision of image retrieval were obtained with average precision 40,5% and recall 8,61% at 18 level orientation and 64 level quantization

Comparison of Methods for Batik Classification Using Multi Texton Histogram

Batik is a symbol reflecting Indonesian culture which has been acknowledged by UNESCO since 2009. Batik has various motifs or patterns. Because most regions in Indonesia have their own characteristic of batik motifs, people find difficulties to recognize the variety of Batik. This study attempts to develop a system that can help people to classify Batik motifs using Multi Texton Histogram (MTH) for feature extraction. Meanwhile, k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) algorithm were employed for classification. The performance of those classifications is then compared to seek the best classification method for Batik classification. The performance is tested 300 images divided into 50 classes. The results show the optimum accuracy achieved using k-NN with k=5 and MTH with 6 textons is 82%; however, SVM and MTH with 6 textons denote 76%. According to the result, MTH as feature extraction, k-NN or SVM as a classifier can be applied on Batik image classification

Image Retrieval Based on Texton Frequency-Inverse Image Frequency

In image retrieval, the user hopes to find the desired image by entering another image as a query. In this paper, the approach used to find similarities between images is feature weighting, where between one feature with another feature has a different weight. Likewise, the same features in different images may have different weights. This approach is similar to the term weighting model that usually implemented in document retrieval, where the system will search for keywords from each document and then give different weights to each keyword. In this research, the method of weighting the TF-IIF (Texton Frequency-Inverse Image Frequency) method proposed, this method will extract critical features in an image based on the frequency of the appearance of texton in an image, and the appearance of the texton in another image. That is, the more often a texton appears in an image, and the less texton appears in another image, the higher the weight. The results obtained indicate that the proposed method can increase the value of precision by 7% compared to the previous method

Geodesics on the manifold of multivariate generalized Gaussian distributions with an application to multicomponent texture discrimination

We consider the Rao geodesic distance (GD) based on the Fisher information as a similarity measure on the manifold of zero-mean multivariate generalized Gaussian distributions (MGGD). The MGGD is shown to be an adequate model for the heavy-tailed wavelet statistics in multicomponent images, such as color or multispectral images. We discuss the estimation of MGGD parameters using various methods. We apply the GD between MGGDs to color texture discrimination in several classification experiments, taking into account the correlation structure between the spectral bands in the wavelet domain. We compare the performance, both in terms of texture discrimination capability and computational load, of the GD and the Kullback-Leibler divergence (KLD). Likewise, both uni- and multivariate generalized Gaussian models are evaluated, characterized by a fixed or a variable shape parameter. The modeling of the interband correlation significantly improves classification efficiency, while the GD is shown to consistently outperform the KLD as a similarity measure

Comparison of Methods for Batik Classification Using Multi Texton Histogram

Batik is a symbol reflecting Indonesian culture which has been acknowledged by UNESCO since 2009. Batik has various motifs or patterns. Because most regions in Indonesia have their own characteristic of batik motifs, people find difficulties to recognize the variety of Batik. This study attempts to develop a system that can help people to classify Batik motifs using Multi Texton Histogram (MTH) for feature extraction. Meanwhile, k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) algorithm were employed for classification. The performance of those classifications is then compared to seek the best classification method for Batik classification. The performance is tested 300 images divided into 50 classes. The results show the optimum accuracy achieved using k-NN with k=5 and MTH with 6 textons is 82%; however, SVM and MTH with 6 textons denote 76%. According to the result, MTH as feature extraction, k-NN or SVM as a classifier can be applied on Batik image classification

Re-Ranking Image Retrieval on Multi Texton Co-Occurrence Descriptor Using K-Nearest Neighbor

Some features commonly used to conduct image retrieval are color, texture and edge. Multi Texton Co-Occurrence Descriptor (MTCD) is a method which uses all three features to perform image retrieval. This method has a high precision when doing retrieval on a patterned image such as Batik images. However, for images focusing on object detection like corel images, its precision decreases. This study proposes the use of KNN method to improve the precision of MTCD method by re-ranking the retrieval results from MTCD. The results show that the method is able to increase the precision by 0.8% for Batik images and 9% for corel images

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