Completed Local Ternary Pattern Research Articles

Surface curvature based completed local ternary pattern for texture image classification

Surface curvature based completed local ternary pattern for texture image classification

Classification of breast cancer images using completed local ternary pattern and support vector machine

Breast cancer is the major cause of deaths in women compared to other cancers. Though early detection of breast cancer reduces cancer deaths, it is a challenging task for physicians. Local binary pattern (LBP) and local ternary pattern (LTP) techniques are widely applied in texture classification applications. Since LBP is more sensitive to noise in texture classification, it needs to be improved for achieving better results. Though LTP is more robust to noise, there are few drawbacks. Completed LBP and completed local binary count techniques achieve good accuracy for texture classification, but they inherit few drawbacks of LBP. In this paper, completed LTP operator is applied on breast cancer images for better classification accuracy than LBP and completed LBP operators, by extracting sign and magnitude components. Experimental results based on breast cancer database show that the proposed technique achieved better classification accuracy than existing similar approaches.

Classification of breast cancer images using completed local ternary pattern and support vector machine

Classification of breast cancer images using completed local ternary pattern and support vector machine

A New Feature-Based Wavelet Completed Local Ternary Pattern (Feat-WCLTP) for Texture Image Classification

LBP is one of the simplest yet most powerful feature extraction descriptors. Many descriptors based on LBP have been proposed to improve its performance. Completed Local Ternary Pattern (CLTP) is one of the important LBP variants that was proposed to overcome LBP's drawbacks. However, despite the impressive performance of CLTP, it suffers from some limitations, such as high dimensionality, thereby leading to higher computation time and may affect the classification accuracy. In this paper, a new rotation invariant texture descriptor (Feat-WCLTP) is proposed. In the proposed Feat-WCLTP descriptor, first the redundant discrete wavelet transform RDWT is integrated with the original CLTP. Then, CLTP is extracted based on the LL wavelet coefficients. Next, the mean and variance features are used to describe the magnitude information instead of using P-dimensional features as the normal magnitude components of CLTP. Reducing the number of extracted features positively affected the computational complexity of the descriptor and the dimensionality of the resultant histogram. The proposed Feat-WCLTP is evaluated using four texture datasets and compared with some well-known descriptors. The experimental results show that Feat-WCLTP outperformed the other descriptors in terms of classification accuracy. It achieves 99.66% in OuTex, 96.89% in CUReT, 95.23% in UIUC and 99.92% in the Kylberg dataset. The experimental results showed that the Feat-WCLTP not only overcomes the CLTP's dimensionality problem but also further improves the classification accuracy.

A New Fast Local Laplacian Completed Local Ternary Count (FLL-CLTC) for Facial Image Classification

Face recognition is one of the most interesting areas of research areas because of its importance in authentication and security. Differentiating between different facial images is not easy because of the similarities in facial features. Human faces can also be covered obscured by eyeglasses, facial expressions and hairstyles can also be changed causing difficulty in finding similar faces. Thus, the need for powerful image features has become a critical issue in the face recognition systems. Many texture features have been used in these systems, including Local Binary Pattern (LBP), Local Ternary Pattern (LTP), Completed Local Binary Pattern (CLBP), Completed Local Binary Count (CLBC) and Completed Local Ternary Pattern (CLTP). In this paper, a new texture descriptor, namely, Completed Local Ternary Count (CLTC), is proposed by adding a threshold value for the CLBC to overcome its sensitivity to noise drawback. The CLTC is also enhanced by adding the Fast-Local Laplacian filter during the pre-processing stage to increase the discriminative property of the proposed descriptor. The proposed Fast-Local Laplacian CLTC (FLL-CLTC) texture descriptor is evaluated for face recognition task using five different face image datasets. The experimental results of the FLL-CLTC showed that the proposed FLL-CLTC outperformed the CLBP and CLTP texture descriptors in term of recognition accuracy. The FLL-CLTC achieved 99.1%, 86.93%, 93.21%, 84.92% and 99.15% with JAFFE, YALE, Georgia Tech, Caltech and ORL face image datasets, respectively.

Noise-tolerant texture feature extraction through directional thresholded local binary pattern

Local binary pattern (LBP) is a multi-applicable texture descriptor applied in machine vision. Despite its outstanding abilities in revealing textural properties of image, it is sensitive to noise, due to its thresholding mechanism. To make LBP robust against noise, a directional thresholded LBP (DTLBP) is developed in this article which applies the directional neighboring pixels average values for thresholding. Applying this type of thresholding in addition to reducing noise, due to using the information of neighboring pixels with bigger radii, increases efficiency in extracting features. The DTLBP is able to be combined with other descriptors like completed LBP (CLBP) and local ternary pattern (LTP) which improves their functionality against noise. To evaluate the functionality of DTLBP, four known datasets including Outex (TC10), CUReT, UIUC and UMD are tested. Numerous and extensive experiments on these datasets with different kinds of noises indicate this newly developed descriptor’s efficiency, with or without incremental white Gaussian and Gaussian blur noises. The proposed descriptor is compared with its available state of the art counterparts. The results show that the combination of DTLBP with CLBP descriptors provide the best classification accuracy in the experiments, which confirms the efficiency and robustness of the proposed descriptor when extracting features from noisy and raw images.

Performance Evaluation of Completed Local Ternary Pattern (CLTP) for Face Image Recognition

Feature extraction is the most important step that affects the recognition accuracy of face recognition. One of these features are the texture descriptors that are playing an important role as local features descriptor in many of the face recognition systems. Recently, many types of texture descriptors had been proposed and used for face recognition task. The Completed Local Ternary Pattern (CLTP) is one of the texture descriptors that has been proposed for texture image classification and had been tested for different image classification tasks. It proposed to overcome the Local Binary Pattern (LBP) drawbacks where the CLTP is more robust to noise as well as shown a good discriminative property than others. In this paper, a comprehensive study on the performance of the CLTP for face recognition task has been done. The aim of this study is to investigate and evaluate the CLTP performance using eight different face datasets and compared with the previous texture descriptors. In the experimental results, the CLTP had been shown good recognition rates and outperformed the other texture descriptors for this task. Several face datasets are used in this paper, such as Georgia Tech Face, Collection Facial Images, Caltech Pedestrian Faces, JAFFE, FEI, YALE, ORL, UMIST datasets.

Wavelet Completed Local Ternary Pattern (WCLTP) for Texture Image Classification

In this paper, a new texture descriptor inspired from Completed Local Ternary Pattern (CLTP) is proposed and investigated for texture image classification task. A wavelet-CLTP (WCLTP) is proposed by integrating the CLTP with the redundant discrete wavelet transform (RDWT). Firstly, the images are decomposed using RDWT into four sub-bands. Then, the CLTP are extracted from the LL sub-bands coefficients of the image. The RDWT is selected due to its advantages. Unlike the other wavelet transform, the RDWT decompose the images into the same size sub-bands. So, the important textures in the image will be at the same spatial location in each sub-band. As a result, more accurate capturing of the local texture within RDWT domain can be done and the exact measure of local texture can be used. The proposed WCLTP is evaluated for rotation invariant texture classification task. The experimental results using CURTex and Outex texture databases show that the proposed WCLTP outperformed the CLBP and CLBC descriptors and achieved an impressive classification accuracy. Furthermore, the WCLTP outperformed the CLTP in Outex and many cases in the CURTex databases.

Medical, Scene And Event Image Category Recognition Using Completed Local Ternary Patterns (CLTP)

Many of texture descriptors are proposed based on the Local Binary Pattern (LBP) and have been achieved remarkable texture classification accuracy such as Completed LBP (CLBP) and Completed Local Binary Count (CLBC). However, the LBP suffers from two weaknesses where: 1) it is sensitive to noise and; 2) it sometimes classify two or more different patterns falsely to the same class. To overcome the LBP weaknesses, we propose a new texture descriptor which is defined as Completed Local Ternary Pattern (CLTP). The CLTP was used for rotation invariant texture classification. It demonstrates superior texture classification accuracy as compared to CLBP and CLBC descriptors. This is because, the CLTP is more robust to noise and has a high discriminating property that achieves impressive classification accuracy rates. In this paper, two types of experiments are carried out. In the first experiment, different amount of additive Gaussian noise is added to the TC10 Outex texture data set to investigate and prove the robustness of the CLTP against the noise. For the second experiment, the performance of CLTP for image category recognition is studied and investigated. A variety of image datasets are used in the experiments such as scene data set (e.g., Oliva and Torralba datasets (OT8)), Event sport datasets, 2D HeLa medical images, and our new scene data set, defined as USM scene data set. The experimental results proved the superiority of the CLTP descriptor over the original LBP, and different new texture descriptors such as CLBP in the image category recognition, as well as the robustness against the noise. In 2D HeLa medical images, the proposed CLTP has achieved the highest state of the art classification rate reaching 95.62%.

Face Recognition Using Completed Local Ternary Pattern (CLTP) Texture Descriptor

Nowadays, face recognition becomes one of the important topics in the computer vision and image processing area. This is due to its importance where can be used in many applications. The main key in the face recognition is how to extract distinguishable features from the image to perform high recognition accuracy. Local binary pattern (LBP) and many of its variants used as texture features in many of face recognition systems. Although LBP performed well in many fields, it is sensitive to noise, and different patterns of LBP may classify into the same class that reduces its discriminating property. Completed Local Ternary Pattern (CLTP) is one of the new proposed texture features to overcome the drawbacks of the LBP. The CLTP outperformed LBP and some of its variants in many fields such as texture, scene, and event image classification. In this study, we study and investigate the performance of CLTP operator for face recognition task. The Japanese Female Facial Expression (JAFFE), and FEI face databases are used in the experiments. In the experimental results, CLTP outperformed some previous texture descriptors and achieves higher classification rate for face recognition task which has reached up 99.38% and 85.22% in JAFFE and FEI, respectively.

Robust personal authentication using finger knuckle geometric and texture features

Abstract This paper investigates on the entire finger dorsal surface for human identity that can be extremely beneficial for forensics applications and its related fields. Further, this paper formulates a novel approach to achieve improved performance by simultaneous extraction and integration of finger knuckle geometric and texture features by score level fusion. The geometric features are derived through Angular Geometric Analysis Method (AGAM) which extracts angular-based feature information for unique identification. Similarly, Texture Feature Extraction Methods (TFEM) viz., Completed Local Ternary Pattern (CLTP) generation method, 2D Log Gabor Filter (2DLGF) method and Fourier – Scale Invariant Feature Transform (F-SIFT) method are incorporated to derive the local texture features of an acquired finger back knuckle surface. The experimental results indicate that integration of geometric and local texture features of finger knuckle regions shows decrease in error rate by 27% (in average) when compared to the existing benchmark system taken for comparison.

Completed local ternary pattern for rotation invariant texture classification.

Despite the fact that the two texture descriptors, the completed modeling of Local Binary Pattern (CLBP) and the Completed Local Binary Count (CLBC), have achieved a remarkable accuracy for invariant rotation texture classification, they inherit some Local Binary Pattern (LBP) drawbacks. The LBP is sensitive to noise, and different patterns of LBP may be classified into the same class that reduces its discriminating property. Although, the Local Ternary Pattern (LTP) is proposed to be more robust to noise than LBP, however, the latter's weakness may appear with the LTP as well as with LBP. In this paper, a novel completed modeling of the Local Ternary Pattern (LTP) operator is proposed to overcome both LBP drawbacks, and an associated completed Local Ternary Pattern (CLTP) scheme is developed for rotation invariant texture classification. The experimental results using four different texture databases show that the proposed CLTP achieved an impressive classification accuracy as compared to the CLBP and CLBC descriptors.

A More Robust Mean Shift Tracker on Joint Color-CLTP Histogram

A more robust mean shift tracker using the joint of color and Completed Local Ternary Pattern (CLTP) histogram is proposed. CLTP is a generalization of Local Binary Pattern (LBP) which can be applied to obtain texture features that are more discriminant and less sensitive to noise. The joint of color and CLTP histogram based target representation can exploit the target structural information efficiently. To reduce the interference of background in target localization, a corrected background-weighted histogram and background update mechanism are adapted to decrease the weights of both prominent background color and texture features similar to the target object. Comparative experimental results on various challenging videos demonstrate that the proposed tracker performs favorably against several variants of state-of-the-art mean shift tracker when heavy occlusions and complex background changes exist.

Discriminative features for texture description

In this paper, a feature extraction method is developed for texture description. To obtain discriminative patterns, we present a learning framework which is formulated into a three-layered model. It can estimate the optimal pattern subset of interest by simultaneously considering the robustness, discriminative power and representation capability of features. This model is generalized and can be integrated with existing LBP variants such as conventional LBP, rotation invariant patterns, local patterns with anisotropic structure, completed local binary pattern (CLBP) and local ternary pattern (LTP) to derive new image features for texture classification. The derived descriptors are extensively compared with other widely used approaches and evaluated on two publicly available texture databases (Outex and CUReT) for texture classification, two medical image databases (Hela and Pap-smear) for protein cellular classification and disease classification, and a neonatal facial expression database (infant COPE database) for facial expression classification. Experimental results demonstrate that the obtained descriptors lead to state-of-the-art classification performance.