A Fast Object Recognition Using Edge Texture Analysis for Image Retrieval

A Robust Object Recognition for Content Based Image Retrieval (CBIR) based on Discriminative Robust Local Binary Pattern (DRLBP) and Local Ternary Pattern (LTP) analysis. The Robust Object Recognition using edge and texture feature extraction. The extension of Local Binary Pattern (LBP) is called DRLBP. The category recognition system will be developed for application to image retrieval. The category recognition is to classify an object into one of several predefined categories. LBP is defined as an ordered set of binary comparisons of pixel intensities between the center pixel and its eight surrounding pixels .DRLBP features identifying the contrast information of image patterns. The proposed features preserve the contrast information of image patterns. The DRLBP discriminates an object like the object surface texture and the object shape formed by its boundary

A new feature-based wavelet completed local ternary pattern (FEAT-WCLTP) for texture and medical image classification

Nowadays, texture image descriptors are used in many important real-life applications. The use of texture analysis in texture and medical image classification has attracted considerable attention. Local Binary Patterns (LBP) is one of the simplest yet eff ective texture descriptors. But it has some limitations that may affect its accuracy. Hence, different variants of LBP were proposed to overcome LBP’s drawbacks and enhance its classification accuracy. Completed local ternary pattern (CLTP) is one of the significant LBP variants. However, CLTP suffers from two main limitations: the selection of the threshold value is manually based and the high dimensionality which is negatively affected the descriptor performance and leads to high computations. This research aims to improve the classification accuracy of CLTP and overcome the computational limitation by proposing new descriptors inspired by CLTP. Therefore, this research introduces two contributions: The first one is a proposed new descriptor that integrates redundant discrete wavelet transform (RDWT) with the original CLTP, namely, wavelet completed local ternary pattern (WCLTP). Extracting CLTP in wavelet transform will help increase the classification accuracy due to the shift invariant property of RDWT. Firstly, the image is decomposed into four sub-bands (LL, LH, HL, HH) by using RDWT. Then, CLTP is extracted based on the LL wavelet coefficients. The latter one is the reduction in the dimensionality of WCLTP by reducing its size and a proposed new texture descriptor, namely, feature-based wavelet completed local ternary pattern (FeatWCLTP). The proposed Feat-WCLTP can enhance CLTP’s performance and reduce high dimensionality. The mean and variance of the values of the selected texture pattern are used instead of the normal magnitude texture descriptor of CLTP. The performance of the proposed WCLTP and Feat-WCLTP was evaluated using four textures (i.e. OuTex, CUReT, UIUC and Kylberg) and two medical (i.e. 2D HeLa and Breast Cancer) datasets then compared with several well-known LBP variants. The proposed WCLTP outperformed the previous descriptors and achieved the highest classification accuracy in all experiments. The results for the texture dataset are 99.35% in OuTex, 96.57% in CUReT, 94.80% in UIUC and 99.88% in the Kylberg dataset. The results for the medical dataset are 84.19% in the 2D HeLa dataset and 92.14% in the Breast Cancer dataset. The proposed Feat-WCLTP not only overcomes the dimensionality problem but also considerably improves the classification accuracy. The results for Feat-WCLTP for texture dataset are 99.66% in OuTex, 96.89% in CUReT, 95.23% in UIUC and 99.92% in the Kylberg dataset. The results for the medical dataset are 84.42% in the 2D HeLa dataset and 89.12% in the Breast Cancer dataset. Moreover, the proposed Feat-WCLTP reduces the size of the feature vector for texture pattern (1,8) to 160 bins instead of 400 bins in WCLTP. The proposed WCLTP and Feat-WCLTP have better classification accuracy and dimensionality than the original CLTP

Local and deep texture features for classification of natural and biomedical images

Developing efficient feature descriptors is very important in many computer vision applications including biomedical image analysis. In the past two decades and before the popularity of deep learning approaches in image classification, texture features proved to be very effective to capture the gradient variation in the image. Following the success of the Local Binary Pattern (LBP) descriptor, many variations of this descriptor were introduced to further improve the ability of obtaining good classification results. However, the problem of image classification gets more complicated when the number of images increases as well as the number of classes. In this case, more robust approaches must be used to address this problem. In this thesis, we address the problem of analyzing biomedical images by using a combination of local and deep features. First, we propose a novel descriptor that is based on the motif Peano scan concept called Joint Motif Labels (JML). After that, we combine the features extracted from the JML descriptor with two other descriptors called Rotation Invariant Co-occurrence among Local Binary Patterns (RIC-LBP) and Joint Adaptive Medina Binary Patterns (JAMBP). In addition, we construct another descriptor called Motif Patterns encoded by RIC-LBP and use it in our classification framework. We enrich the performance of our framework by combining these local descriptors with features extracted from a pre-trained deep network called VGG-19. Hence, the 4096 features of the Fully Connected 'fc7' layer are extracted and combined with the proposed local descriptors. Finally, we show that Random Forests (RF) classifier can be used to obtain superior performance in the field of biomedical image analysis. Testing was performed on two standard biomedical datasets and another three standard texture datasets. Results show that our framework can beat state-of-the-art accuracy on the biomedical image analysis and the combination of local features produce promising results on the standard texture datasets.Includes bibliographical reference

Doku tanımada şekil bilgisi kullanarak yeni özniteliklerin elde edilmesi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Bu tezde, doku tanımada başarılı bir şekilde kullanılan yerel ikili örüntüler (Local Binary Pattern - LBP) öznitelik çıkarım yönteminin komşuluk topolojisi üzerinde çalışılmıştır. LBP, görüntünün gri seviye değerlerini kullanan basit bir öznitelik çıkarım yöntemidir. Görüntüdeki her piksel referans piksel olarak belirlenir ve etrafındaki komşuları dairesel bir topoloji ile seçilir. Bu tez çalışması kapsamında LBP yöntemi ile farklı spiral topolojilere sahip komşuluklar kullanılarak daha anlamlı özniteliklerin elde edilmesi amaçlanmıştır. Ayrıca, özniteliklerin ayırt edici özelliklerini arttırmak amacıyla, spiral topoloji kullanan LBP kodları ile görüntünün kenar bilgileri birleştirilmiştir. Histogram tabanlı öznitelik vektörlerinin sınıflandırılmasında genellikle benzerlik ölçütleri kullanılmaktadır. Yapılan tezde, doku tanımada sınıflandırma aşamasında altuzay tabanlı yöntemler ile tanıma başarımının artırılması hedeflenmiştir. Önerilen öznitelik çıkarım yöntemi, doku tanımada CURet ve UIUC doku veritabanları, yüz tanımada ise AR ve ORL yüz veritabanları üzerinde çeşitli deneyler ile test edilmiştir. Yapılan deneyler sonucunda, CURet doku veritabanında % 99,6'lara varan bir tanıma oranı elde edilmiştir. Farklı LBP varyasyonları ile karşılaştırıldığında, daha iyi sınıflandırma başarımları elde edilmiştir. Ayrıca geliştirdiğimiz öznitelik çıkarım yöntemi ile elde edilen öznitelik vektör boyutunu azaltmak amacıyla yeni bir öznitelik seçim algoritması önerilmiştir. Önerilen yöntemde sınıflar arası varyansın olabildiğince yüksek hale getirilmesi amaçlanmaktadır. Önerilen öznitelik seçimi yönteminin başarımı doku tanımada test edilmiştir. Deney sonuçlarına göre, öznitelik vektörünün boyutu %99'a kadar azaldığında tanıma oranı sadece %0,06 azalmaktadır.In this thesis, neighborhood topology of Local Binary Pattern (LBP) which is used successfully applied in texture recognition is studied. LBP is a simple feature extraction method that uses the gray-level values of an image. Each pixel in the image is determined as the reference pixel and its surrounding neighbors are selected over a circular topology. In this thesis work, by using the LBP method, it is aimed to obtain more meaningful features, by using neighbors with different spiral topologies. In addition, the LBP codes which use spiral topology is concatenated with the edge information of the image to enhance the distinctive characteristics of the features. Similarity measures are generally used to classify histogram-based feature vectors. In this thesis, it is aimed to increase the recognition performance with subspace based methods in the classification stage of texture recognition. Proposed feature extraction method is tested with several experiments on CURet and UIUC texture database in texture recognition, AR and ORL face database in face recognition. As a result of the experiments, the recognition rate is acquired up to %99,6 on CURet texture database. Better classification performances are achieved when compared to the several LBP variations. In addition, a new feature selection algorithm is proposed to reduce the dimension of the feature vectors obtained by the proposed feature extraction method. In the proposed method, it is aimed to make the between-class variance as high as possible. The performance of the proposed feature selection method is tested in texture recognition. The experimental results suggest that, the recognition rate is reduced by only 0.06% when the dimension of the feature vector is reduced up to 99%

Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted ncomponent of this work in other works.Efficient defect classification is one of the most important preconditions to achieve online quality inspection for hot-rolled strip steels. It is extremely challenging owing to various defect appearances, large intraclass variation, ambiguous interclass distance, and unstable gray values. In this paper, a generalized completed local binary patterns (GCLBP) framework is proposed. Two variants of improved completed local binary patterns (ICLBP) and improved completed noise-invariant local-structure patterns (ICNLP) under the GCLBP framework are developed for steel surface defect classification. Different from conventional local binary patterns variants, descriptive information hidden in nonuniform patterns is innovatively excavated for the better defect representation. This paper focuses on the following aspects. First, a lightweight searching algorithm is established for exploiting the dominant nonuniform patterns (DNUPs). Second, a hybrid pattern code mapping mechanism is proposed to encode all the uniform patterns and DNUPs. Third, feature extraction is carried out under the GCLBP framework. Finally, histogram matching is efficiently accomplished by simple nearest-neighbor classifier. The classification accuracy and time efficiency are verified on a widely recognized texture database (Outex) and a real-world steel surface defect database [Northeastern University (NEU)]. The experimental results promise that the proposed method can be widely applied in online automatic optical inspection instruments for hot-rolled strip steel.Peer reviewe