2D-DOST Özellik Çıkarımı Yöntemi ve LS-SVM Sınıflandırıcı Tabanlı Doku Sınıflandırma Sistemi

Bu çalışmada, doku görüntülerinin sınıflandırılması için 2D-DOST (İki Boyutlu Ayrık Orthonormal Stockwell Dönüşümü) ve LS-SVM (En Küçük Kareler Destek Vektör Makineleri) tabanlı yeni bir sınıflandırıcı sistemi önerilmiştir. Önerilen sistem, özellik çıkarımı ve sınıflandırma olmak üzere iki ana bölümden oluşmaktadır. Özellik çıkarımı aşamasında, görüntüleri temsil eden ait ayırt edici özellik vektörleri 2D-DOST tabanlı özellik çıkarım yöntemi ile elde edilmektedir. Sınıflandırma aşamasında ise yüksek başarım oranı ve doğruluğa sahip olan LS-SVM sınıflandırıcısı ile doku görüntüleri sınıflandırılmaktadır. LS-SVM’nin eğitimi her bir doku görüntüsüne ait ayırt edici özellik vektörleri üzerinde gerçekleştirilmiştir. Test verisi olarak hazırlanan doku görüntüleri eğitilmiş LS-SVM sınıflandırıcının girişine uygulanmıştır. Önerilen yöntemin performans testleri için alt-görüntüler ile elde edilen farklı veri setleri üzerinde gerçekleştirilmiştir. Bu veri setleri hem normal doku görüntülerini hem de gürültülü doku görüntülerini içermektedir. Veri setleri içerisindeki alt görüntüler Brodatz ve Kylberg doku veri tabanlarından türetilmiştir. Gürültülü verilerin oluşturulmasında, farklı seviyelerde Gaussian ve Tuz&Biber gürültüsü kullanılmıştır. Çalışma sonuçları, önerilen 2D-DOST ve LS-SVM tabanlı sınıflandırıcının doku görüntülerini yüksek başarım oranı ile sınıflandırabildiğini ve gürültüye karşı gürbüz olduğunu göstermektedir.

Texture Classification System Based on 2D-DOST Feature Extraction Method and LS-SVM Classifier

In this paper, a new 2D-DOST (Two-Dimensional Discrete Orthonormal Stockwell Transform) and LS-SVM (Least Squares Support Vector Machines) based classifier system is proposed for classification of texture images. The proposed system contains two main stages. These stages are feature extraction and classification. In the feature extraction stage, the distinguishing feature vectors which represent descriptive features of texture images are obtained by using a 2D-DOST based feature extraction method. In the classification stage, the texture images are classified by the LS-SVM since this classifier has high success rate and accuracy. The training of LS-SVM is performed on the distinguishing feature vector of each texture component. Texture samples are recognized by the test data applied to the input of trained LS-SVM classifier. Performance evaluations of the proposed method are carried on different datasets obtained from sub-images. These datasets include both the normal texture images and noise added images. Sub-images into datasets are derived from Brodatz and Kylberg texture images database. Gaussian and Salt & Pepper noise with different levels are used for creating noisy datasets. According to the study results, the proposed 2D-DOST and LS-SVM based classifier has a capability of classifying texture images with high success rate and noise robustness.

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