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An efficient texture classification algorithm using integrated Discrete Wavelet Transform and local binary pattern features

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COGNITIVE SYSTEMS RESEARCH 2018年 52卷 267-274页

作者： Krishnan, K. Gopala Vanathi, P. T. Mepco Schlenk Engn Coll ECE Dept Sivakasi India PSG Coll Technol ECE Dept Coimbatore Tamil Nadu India

This manuscript is keen to the Texture Classification problem. Texture is mainly defined as measuring the variation in the surface intensity such as regularity, smoothness, coarseness, etc. Texture classification is one of the most important issues in image processing and computer vision. Orientation, scale, image transitions or singularities such as edges, and the other visual appearance are the major problems in texture classification. Already works have done in texture classification by using Discrete Wavelet Transforms (DWT) and local binary pattern (LBP) separately. The above techniques give minimum classification Accuracy. LBP is considered as an effective method but its performance is lower if the image has poor quality. We propose a technique to characterize the texture properties based on DWT using local binary pattern. In this proposed work, input texture images are decomposed using single level Discrete Wavelet Transform. Then LBP features are extracted from all sub bands. The extracted LBP features for sub bands are combined to form main feature (1024 features). Image classification is done by using k-Nearest Neighbour (kNN) Classifier. The experiments validation are achieved by using four standard data sets (KTH-TIPS, KTH-TIPS-2a, Brodatz and Curet). The results are compared with Dense Micro block Difference (DMD) feature descriptors. The experimental result shows that the proposed method outperforms than the existing techniques. Also reduce the computational complexity and minimum computational time than the existing classification techniques. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Texture classification local binary pattern Discrete Wavelet Transform k-NN classifier

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Discriminative local binary pattern

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MACHINE VISION AND APPLICATIONS 2016年第8期27卷 1175-1186页

作者： Kobayashi, Takumi Natl Inst Adv Ind Sci & Technol 1-1-1 Umezono Tsukuba Ibaraki Japan

local binary pattern (LBP) is widely used to extract image features as well as motion features in various visual recognition tasks. LBP is formulated in quite a simple form and thus enables us to extract effective features with a low computational cost. There, however, are some limitations mainly regarding sensitivity to noise and loss of image contrast information. In this paper, we propose a novel LBP-based feature extraction method to remedy those drawbacks without degrading the simplicity of the original LBP formulation. LBP is built upon encoding local pixel intensities into binary patterns which can be regarded as separating them into two modes (clusters). We introduce Fisher discriminant criterion to optimize the LBP coding for exploiting binary patterns more stably and discriminatively with robustness to noise. Besides, image contrast information is incorporated in a unified way by leveraging the discriminant score as a weight on the binary pattern;therefore, the prominent patterns, such as around edges, are emphasized. The proposed method is applicable to extract not only image features but also motion features by both efficiently decomposing a XYT volume patch into 2-D patches and employing the effective thresholding strategy based on the volume patch. In the experiments on various visual recognition tasks, the proposed method exhibits superior performance compared to the ordinary LBP and the other methods.

关键词： Visual recognition Image feature local binary pattern Discriminant criterion

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Clutter suppression algorithm based on bidirectional local binary pattern for moving point target detection in infrared image sequences

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OPTICAL ENGINEERING 2010年第10期49卷 107005-107005页

作者： Cheng, Zhao Zhang, Tianxu Yan, Luxin Wang, Xiaoping Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Hubei Peoples R China

A clutter suppression algorithm called the rectification filter with indications of bidirectional local binary patterns (BDLBP-RF) is proposed as a resolution to the problem of detecting dim targets in infrared (IR) image sequences. First of all, a local binary pattern (LBP) operator with properties of grayscale and rotation invariance is introduced in the application of clutter suppression. Each pixel in the image is estimated by its spatial neighbor pixels and the corresponding LBPs in prior and posterior frames. The approach proposed is based on a spatiotemporal process, in which interframe and intraframe properties of the IR image sequence are both taken into account. The method is evaluated by the comparative experiments, and the LBP operator's optimum values of radius and number of neighbors are discussed. The results of the experiment prove that BDLBP-RF has excellent performance and stability in clutter suppression under various situations. The target point in images processed by our approach received high signal-to-clutter ratio gain, and the detectability of the target is enhanced. (C) 2010 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.3497569]

关键词： clutter suppression local binary pattern point target detection spatiotemporal filter

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Integrating adaptive neuro-fuzzy inference system and local binary pattern operator for robust retinal blood vessels segmentation

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NEURAL COMPUTING & APPLICATIONS 2013年第1-sup期22卷 S163-S174页

作者： Fathi, Abdolhossein Naghsh-Nilchi, Ahmad Reza Univ Isfahan Dept Comp Engn Esfahan Iran

Automatic extraction of blood vessels is an important step in computer-aided diagnosis in ophthalmology. The blood vessels have different widths, orientations, and structures. Therefore, the extracting of the proper feature vector is a critical step especially in the classifier-based vessel segmentation methods. In this paper, a new multi-scale rotation-invariant local binary pattern operator is employed to extract efficient feature vector for different types of vessels in the retinal images. To estimate the vesselness value of each pixel, the obtained multi-scale feature vector is applied to an adaptive neuro-fuzzy inference system. Then by applying proper top-hat transform, thresholding, and length filtering, the thick and thin vessels are highlighted separately. The performance of the proposed method is measured on the publicly available DRIVE and STARE databases. The average accuracy 0.942 along with true positive rate (TPR) 0.752 and false positive rate (FPR) 0.041 is very close to the manual segmentation rates obtained by the second observer. The proposed method is also compared with several state-of-the-art methods. The proposed method shows higher average TPR in the same range of FPR and accuracy.

关键词： Retinal image segmentation Blood vessel detection local binary pattern Adaptive neuro-fuzzy inference system

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Face recognition algorithm based on self-adaptive blocking local binary pattern

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第16期80卷 23899-23921页

作者： Shi, Dongmei Tang, Hongyu Suzhou Coll Informat Technol Dept Comp Sci Suzhou Peoples R China Zhenjiang Coll Sch Elect & Informat Zhenjiang Jiangsu Peoples R China

Face recognition is a common means of identity authentication. Mobile learning platform login technology has developed from user name and password to face recognition. In order to improve effectively the rate of face recognition, this paper proposes a kind of face recognition algorithm based on self-adaptive blocking local binary pattern (LBP) and dual channel convolutional neural network (CNN) with different convolution kernels. Firstly, the Gamma correction, the Mallet wavelet filtering and normalization are used to preprocess the face image. The face image is decomposed and reconstructed by 2-layer Mallet wavelet to filter out the interference signal effectively. Although the general LBP operator extracts the overall texture and contour features of the face image, the distribution of the bright spot, dark spot and other micro details cannot be fully characterized. In order to solve this problem, integral projection is introduced to project the image horizontally and vertically. The extreme points of the projection represent the texture mutation points of the face image in the horizontal and vertical directions. These extreme points are used as the boundary of the image blocking, and the LBP value of the face image is extracted by the self-adaptive blocking strategy. Combining the features of k-nearest neighbor classifier and softmax, a k-softmax classification method is proposed to classify and recognize the face image labels. After two channel network structure training, this method is tested on Yale, ORL, extended Yale B and self-built face databases by five experiments, comparing with other face recognition algorithms. The results show that the proposed method based on SAB-LBP and dual channel CNN has high recognition rate and computational efficiency.

关键词： Face recognition local binary pattern Convolutional neural network Mobile learning Image

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A Heteromorphous Deep CNN Framework for Medical Image Segmentation Using local binary pattern

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IEEE ACCESS 2022年 10卷 63466-63480页

作者： Iqbal, Saeed Qureshi, Adnan N. Univ Cent Punjab Fac Informat Technol Dept Comp Sci Lahore 54782 Pakistan

Estimating mitotic nuclei in breast cancer samples can aid in determining the tumor's aggressiveness and grading system. Because of their strong resemblance to non-mitotic nuclei and heteromorphic form, automated evaluation of mitotic nuclei is difficult. This study presents the BreastUNet, a new heteromorphous Deep Convolutional Neural Network (CNN) with feature grafting approach for analysing mitotic nuclei in breast histopathology images. In the first stage, the proposed method identifies probable mitotic patches in histopathological imaging regions, and in the second stage, the proposed model classifies these patches into mitotic and non-mitotic nuclei. For the building of a heteromorphous deep CNN, four distinct deep CNNs are developed and used as the basis CNN model. Deep CNNs with various architectural designs capture the structural, textural, and morphological aspects of mitotic nuclei. The performance of the proposed BreastUNet model is compared to those of state-of-the-art CNNs. The proposed model looks superior on the test set, with an F1 score of 0.95, Sensitivity and Specificity is 0.95 and area under the precision curve of 0.95. The recommended hybrid high F1 score and precision, as well as its excellent generalization and accuracy, imply that it might be used to build a pathologist's aid tool.

关键词： Breast cancer Feature extraction Convolutional neural networks Image segmentation Breast Training Medical diagnostic imaging Convolutional neural network breast cancer mitosis detection histopathology hand-crafted feature segmentation local binary pattern

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Texture classification combining improved local binary pattern and threshold segmentation

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第17期82卷 25899-25916页

作者： Luo, Yijie Sa, Jiming Song, Yuyan Jiang, He Zhang, Chi Zhang, Zhushanying Wuhan Univ Technol Sch Informat Engn Wuhan 430070 Peoples R China Wuhan Univ Technol Hubei Key Lab Broadband Wireless Commun & Sensor N Wuhan 430070 Peoples R China Hubei Key Lab Med Informat Anal & Tumor Diag & Tre Wuhan 430074 Peoples R China South Cent Minzu Univ Coll Biomed Engn Wuhan 430074 Peoples R China South Cent Minzu Univ Key Lab Cognit Sci State Ethn Affairs Commiss Wuhan 430074 Peoples R China

local binary pattern (LBP) is a feature extraction operator with both high texture discrimination ability and low computational complexity. Many LBP variants have been proposed to improve the performance of texture classification or overcome the drawbacks of LBP. There are three shortcomings in some LBP variants: discarding the magnitude component between local differences, adopting fixed weights in the encoding process and discarding the absolute information of the pixel gray level. Based on the three points, this paper proposes an improved LBP with two operators, local binary pattern operator based on magnitude ranking and global threshold segmentation operator, to further improve the performance. This improved LBP can achieve excellent texture classification accuracy across six common datasets, with an average of 1% lower than the best LBP variants. Meanwhile, the computational complexity of the proposed improved LBP is several times lower than that of the best LBP variants.

关键词： Texture classification local binary pattern Feature extraction

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Microscopic local binary pattern for Texture Classification

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2012年第9期E95A卷 1587-1595页

作者： He, Jiangping Song, Wei Ji, Hongwei Yang, Xin Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200030 Peoples R China

This paper presents a Microscopic local binary pattern (MLBP) for texture classification. The conventional LBP methods which rely on the uniform patterns discard some texture information by merging the nonuniform patterns. MLBP preserves the information by classifying the nonuniform patterns using the structure similarity at microscopic level. First, the nonuniform patterns are classified into three groups using the macroscopic information. Second, the three groups are individually divided into several subgroups based on the microscopic structure information. The experiments show that MLBP achieves a better result compared with the other LBP related methods.

关键词： texture analysis local binary pattern microscopic level structure information

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Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques

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COMPUTERS IN BIOLOGY AND MEDICINE 2019年 105卷 72-80页

作者： Maheshwari, Shishir Kanhangad, Vivek Pachori, Ram Bilas Bhandary, Sulatha, V Acharya, U. Rajendra Indian Inst Technol Indore Discipline Elect Engn Indore 453552 Madhya Pradesh India Kasturba Med Coll & Hosp Dept Ophthalmol Manipal 576104 Karnataka India Ngee Ann Polytech Dept Elect & Commun Engn Singapore 599489 Singapore SUSS Univ Sch Sci & Technol Dept Biomed Engn Singapore 599491 Singapore Taylors Univ Fac Hlth & Med Sci Sch Med Subang Jaya 47500 Malaysia

Background and objective: Glaucoma is a ocular disorder which causes irreversible damage to the retinal nerve fibers. The diagnosis of glaucoma is important as it may help to slow down the progression. The available clinica methods and imaging techniques are manual and require skilled supervision. For the purpose of mass screening an automated system is needed for glaucoma diagnosis which is fast, accurate, and helps in reducing the burdei on experts. Methods: In this work, we present a bit-plane slicing (BPS) and local binary pattern (LBP) based novel approad for glaucoma diagnosis. Firstly, our approach separates the red (R), green (G), and blue (B) channels from th input color fundus image and splits the channels into bit planes. Secondly, we extract LBP based statistics features from each of the bit planes of the individual channels. Thirdly, these features from the individua channels are fed separately to three different support vector machines (SVMs) for classification. Finally, th. decisions from the individual SVMs are fused at the decision level to classify the input fundus image into norma or glaucoma class. Results: Our experimental results suggest that the proposed approach is effective in discriminating normal am glaucoma cases with an accuracy of 99.30% using 10-fold cross validation. Conclusions: The developed system is ready to be tested on large and diverse databases and can assist th ophthalmologists in their daily screening to confirm their diagnosis, thereby increasing accuracy of diagnosis.

关键词： Glaucoma Bit-plane slicing local binary pattern Support vector machine Decision level fusion

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Dynamic texture recognition based on completed volume local binary pattern

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MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING 2016年第2期27卷 563-575页

作者： Tiwari, Deepshikha Tyagi, Vipin Jaypee Univ Engn & Technol Guna 473226 India

Dynamic textures are the sequences of images of moving scenes having some stationary properties in time;these include sea-waves, smoke, foliage, whirlwind etc. In recent years, dynamic texture description and recognition has attracted growing attention. This paper presents a more effective completed modeling of volume local binary pattern (VLBP) to recognize dynamic textures. Due to the dependency on local binary pattern (LBP), traditional VLBP also suffers with noise sensitivity and may give the same LBP code to different structural patterns;thus limiting the discriminating power of a texture descriptor. To represent temporal textures we have used VLBP. local region of a volume is represented using its center frame and local sign magnitude difference with the circularly symmetric neighborhood. We have proposed a new contrast operator to complement the sign information of the temporal texture. To add additional discriminative information, volume center pixel information is also fused with the sign magnitude difference of texture. By combining these features into hybrid distributions we get higher classification accuracy for rotation invariant texture classification. Experimental results on UCLA and Dyntex databases show that the proposed approach provides better performance in comparison to the existing approaches.

关键词： Temporal texture local binary pattern Dynamic texture representation

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