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X-ray Image Classification Using Random Forests with local Wavelet-Based CS-local binary patterns

JOURNAL OF DIGITAL IMAGING 2011年第6期24卷 1141-1151页

作者： Ko, Byoung Chul Kim, Seong Hoon Nam, Jae-Yeal Keimyung Univ Dept Comp Engn Taegu 704701 South Korea

This paper presents a fast and efficient method for classifying X-ray images using random forests with proposed local wavelet-based local binary pattern (LBP) to improve image classification performance and reduce training and testing time. Most studies on local binary patterns and its modifications, including centre symmetric LBP (CS-LBP), focus on using image pixels as descriptors. To classify X-ray images, we first extract local wavelet-based CS-LBP (WCS-LBP) descriptors from local parts of the images to describe the wavelet-based texture characteristic. Then we apply the extracted feature vector to decision trees to construct random forests, which are an ensemble of random decision trees. Using the random forests with local WCS-LBP, we classified one test image into the category having the maximum posterior probability. Compared with other feature descriptors and classifiers, the proposed method shows both improved performance and faster processing time.

关键词： X-ray image classification Random forests local binary patterns Image analysis Pattern recognition Automated Decision trees Diagnostic imaging Digital image management

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On the Occurrence Probability of local binary patterns: A Theoretical Study

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JOURNAL OF MATHEMATICAL IMAGING AND VISION 2011年第3期40卷 259-268页

作者： Bianconi, Francesco Fernandez, Antonio Univ Perugia Dipartimento Ingn Ind I-06125 Perugia Italy Univ Vigo Escuela Ingn Ind Vigo 36310 Spain

It is well-known that local binary pattern (LBP) histograms of real textures exhibit a markedly uneven distribution, which is dominated by the so-called uniform patterns. The widely accepted interpretation of this phenomenon is that uniform patterns correspond to texture microfeatures, such as edges, corners, and spots. In this paper we present a theoretical study about the relative occurrence of LBPs based on the consideration that the LBP operator partitions the set of grayscale patterns into an ensemble of disjoint multidimensional polytopes. We derive exact prior probabilities of LBPs by calculating the volume of such polytopes. Our study puts in evidence that both the uneven distribution of the LBP histogram and the high occurrence of uniform patterns are direct consequences of the mathematical structure of the method rather than an intrinsic property of real textures.

关键词： local binary patterns Texture Polytopes

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Wavelet local binary patterns fusion as illuminated facial image preprocessing for face verification

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第4期38卷 3959-3972页

作者： Goh, Yi Zheng Teoh, Andrew Beng Jin Goh, Michael Kah Ong Yonsei Univ Coll Engn Sch Elect & Elect Engn Seoul 120749 South Korea Multimedia Univ FIST Melaka 75450 Malaysia

Poor illumination condition is recognized as one of the major problem in contemporary two-dimensional (2D) face verification system. It causes large variation in facial images and degrades the performance of the system. Many works of resolving illumination variation in face verification have been reported in the past decades. In this paper, a facial image illumination invariant technique is devised based on the fusion of wavelet analysis and local binary patterns. Particularly, illumination-reflectance model is used to detach illumination and reflectance components with multi-resolution nature of wavelet analysis. The illumination component that resides in low spatial-frequency wavelet subband is first rid off efficiently. The reflectance components that reside in high and middle spatial-frequency wavelet subbands are enhanced with local binary patterns histogram. Finally, two processed images are fused through wavelet image fusion. This technique works out promisingly in achieving better recognition results on YaleB, CMU PIE and FRGC face databases in comparison with existing illumination invariant techniques. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Illumination-reflectance model Wavelet local binary patterns Wavelet fusion Histogram specification Preprocessing

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Automated artifact detection and removal for improved tensor estimation in motion-corrupted DTI data sets using the combination of local binary patterns and 2D partial least squares

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MAGNETIC RESONANCE IMAGING 2011年第2期29卷 230-242页

作者： Zhou, Zhenyu Liu, Wei Cui, Jiali Wang, Xunheng Arias, Diana Wen, Ying Bansal, Ravi Hao, Xuejun Wang, Zhishun Peterson, Bradley S. Xu, Dongrong Columbia Univ Dept Psychiat Pediat Brain Imaging Lab MRI Unit New York NY 10032 USA New York State Psychiat Inst & Hosp Pediat Brain Imaging Lab New York NY 10032 USA Minist Educ Key Lab Brain Funct Genom Shanghai Peoples R China E China Normal Univ Shanghai Key Lab Brain Funct Gen Shanghai 20062 Peoples R China E China Normal Univ Shanghai Key Lab Magnet Resonance Shanghai 20062 Peoples R China

Signal variation in diffusion-weighted images (DWIs) is influenced both by thermal noise and by spatially and temporally varying artifacts, such as rigid-body motion and cardiac pulsation. Motion artifacts are particularly prevalent when scanning difficult patient populations, such as human infants. Although some motion during data acquisition can be corrected using image coregistration procedures, frequently individual DWIs are corrupted beyond repair by sudden, large amplitude motion either within or outside of the imaging plane. We propose a novel approach to identify and reject outlier images automatically using local binary patterns (LBP) and 2D partial least square (2D-PLS) to estimate diffusion tensors robustly. This method uses an enhanced LBP algorithm to extract texture features from a local texture feature of the image matrix from the DWI data. Because the images have been transformed to local texture matrices, we are able to extract discriminating information that identifies outliers in the data set by extending a traditional one-dimensional PLS algorithm to a two-dimension operator. The class-membership matrix in this 2D-PLS algorithm is adapted to process samples that are image matrix, and the membership matrix thus represents varying degrees of importance of local information within the images. We also derive the analytic form of the generalized inverse of the class-membership matrix. We show that this method can effectively extract local features from brain images obtained from a large sample of human infants to identify images that are outliers in their textural features, permitting their exclusion from further processing when estimating tensors using the DWIs. This technique is shown to be superior in performance when compared with visual inspection and other common methods to address motion-related artifacts in DWI data. This technique is applicable to correct motion artifact in other magnetic resonance imaging (MRI) techniques (e.g., the bootstra

关键词： DWI Tensor estimation local binary patterns 2D partial least square Outlier Infant

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Facial Expression Recognition Based on local binary patterns and Kernel Discriminant Isomap

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SENSORS 2011年第10期11卷 9573-9588页

作者： Zhao, Xiaoming Zhang, Shiqing Taizhou Univ Dept Comp Sci Taizhou 317000 Peoples R China Taizhou Univ Sch Phys & Elect Engn Taizhou 318000 Peoples R China

Facial expression recognition is an interesting and challenging subject. Considering the nonlinear manifold structure of facial images, a new kernel-based manifold learning method, called kernel discriminant isometric mapping (KDIsomap), is proposed. KDIsomap aims to nonlinearly extract the discriminant information by maximizing the interclass scatter while minimizing the intraclass scatter in a reproducing kernel Hilbert space. KDIsomap is used to perform nonlinear dimensionality reduction on the extracted local binary patterns (LBP) facial features, and produce low-dimensional discrimimant embedded data representations with striking performance improvement on facial expression recognition tasks. The nearest neighbor classifier with the Euclidean metric is used for facial expression classification. Facial expression recognition experiments are performed on two popular facial expression databases, i.e., the JAFFE database and the Cohn-Kanade database. Experimental results indicate that KDIsomap obtains the best accuracy of 81.59% on the JAFFE database, and 94.88% on the Cohn-Kanade database. KDIsomap outperforms the other used methods such as principal component analysis (PCA), linear discriminant analysis (LDA), kernel principal component analysis (KPCA), kernel linear discriminant analysis (KLDA) as well as kernel isometric mapping (KIsomap).

关键词： kernel isometric mapping dimensionality reduction local binary patterns facial expression recognition

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Spectral and Spatial Classification of Hyperspectral Images Based on Random Multi-Graphs

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REMOTE SENSING 2018年第8期10卷 1271-1271页

作者： Gao, Feng Wang, Qun Dong, Junyu Xu, Qizhi Ocean Univ China Coll Informat Sci & Engn Qingdao 266100 Peoples R China Ocean Univ China Qingdao Key Lab Mixed Real & Virtual Ocean Qingdao 266100 Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing Key Lab Digital Media Beijing 100191 Peoples R China

Hyperspectral image classification has been acknowledged as the fundamental and challenging task of hyperspectral data processing. The abundance of spectral and spatial information has provided great opportunities to effectively characterize and identify ground materials. In this paper, we propose a spectral and spatial classification framework for hyperspectral images based on Random Multi-Graphs (RMGs). The RMG is a graph-based ensemble learning method, which is rarely considered in hyperspectral image classification. It is empirically verified that the semi-supervised RMG deals well with small sample setting problems. This kind of problem is very common in hyperspectral image applications. In the proposed method, spatial features are extracted based on linear prediction error analysis and local binary patterns;spatial features and spectral features are then stacked into high dimensional vectors. The high dimensional vectors are fed into the RMG for classification. By randomly selecting a subset of features to create a graph, the proposed method can achieve excellent classification performance. The experiments on three real hyperspectral datasets have demonstrated that the proposed method exhibits better performance than several closely related methods.

关键词： random multi-graphs local binary patterns hyperspectral image pattern classification

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Influence of normalization and color space to color texture classification

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PATTERN RECOGNITION 2017年第0期61卷 120-138页

作者： Cernadas, E. Fernandez-Delgado, M. Gonzalez-Rufino, E. Carrion, P. Univ Santiago de Compostela Ctr Singular Invest Tecnol Informac USC CiTIUS Rua Xenaro Fuente Dominguez Santiago De Compostela 15782 Spain Univ Vigo Dept Comp Sci Campus Univ As Lagoas Orense Spain

Color texture classification has recently attracted significant attention due to its multiple applications. The color texture images depend on the texture surface and its albedo, the illumination, the camera and its viewing position. A key problem to get an acceptable performance is the ambient illumination, which can vary the perceived structures in the surface. Given a color texture classification problem, it would be desirable to know which is the best approach to solve the problem making the minimal assumptions about the illumination conditions. The present work does an exhaustive evaluation of the state-of-the-art color texture classification methods, considering 5 different color spaces, 12 normalization methods to achieve illumination invariances, 19 texture feature vectors and 23 pure color feature vectors. Our experiments allow to conclude that parallel approaches are better than integrative approaches for color texture classification achieving the first positions in the Friedman ranking. Multiresolution local binary patterns (MLBP) are the best intensity texture features, followed by wavelet and Gabor filters combined with luminance-chrominance color spaces (Lab and Lab2000(HL)), and for pure color classification the best are First Order Statistics (FOS) calculated in RGB color space. For intensity texture features, the learning methods work better on the four smallest datasets, although they could not be tested in other four bigger datasets due to its huge computational cost, nor with color texture classification. Normalization and color spaces slightly increase the average accuracy of color texture classification, although the differences achieved using normalization are not statistically significant in a paired T-Test. Lab2000HL and RGB are the best color spaces, but the former is the slowest one. Regarding elapsed time, the best vector features MLBP for intensity texture, Daub4 (Daubechies filters using mean and variance statistics) for color texture a

关键词： Color texture classification Color space Image normalization Wavelet transforms Gabor transforms local binary patterns Learning methods

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Performance evaluation of time-frequency image feature sets for improved classification and analysis of non-stationary signals: Application to newborn EEG seizure detection

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KNOWLEDGE-BASED SYSTEMS 2017年 132卷 188-203页

作者： Boashash, Boualem Barki, Hichem Ouelha, Samir Qatar Univ Dept Elect Engn POB 2713 Doha Qatar Univ Queensland Ctr Clin Res Brisbane Qld Australia Siemens WLL Innovat Ctr Qatar Sci & Technol PkPOB 21757 Doha Qatar

This study demonstrates that a time-frequency (TF) image pattern recognition approach offers significant advantages over standard signal classification methods that use t-domain only or f-domain only features. Two approaches are considered and compared. The paper describes the significance of the standard TF approach for non-stationary signals;TF signal (TFS) features are defined by extending t-domain or (domain features to a joint (t, f) domain resulting in e.g. TF flatness and TF flux. The performance of the extended TFS features is comparatively assessed using Receiver Operating Characteristic (ROC) analysis Area Under the Curve (AUC) measure. Experimental results confirm that the extended TFS features generally yield improved performance (up to 19%) when compared to the corresponding t-domain and f-domain features. The study also explores a second approach based on novel TF image (TFI) features that further improves TF-based classification of non-stationary signals. New TFI features are defined and extracted from the (t, f) domain;they include TF Hu invariant moments, TF Haralick features, and TF local binary patterns (LBP). Using a state-of-the-art classifier, different metrics based on confusion matrix performance are compared for all extended TFS features and TFI features. Experimental results show the improved performance of TFI features over both TFS features and t-domain only or f-domain only features, for all TF representations and for all the considered performance metrics. The experiment is validated by comparing this new proposed methodology with a recent study, utilizing the same large and complex data set of EEG signals, and the same experimental setup. The resulting classification results confirm the superior performance of the proposed TFI features with accuracy improvement up to 5.52%. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Machine learning Non-Stationary signal analysis Hu invariant moments Haralick features local binary patterns Time-frequency distributions Random forests

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Robust method for interest region description based on local intensity binary pattern

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JOURNAL OF ELECTRONIC IMAGING 2017年第4期26卷 43025.1-43025.1页

作者： Yang, Yi Duan, Fajie Jiang, Jiajia Ma, Ling Zheng, Hao Tianjin Univ State Key Lab Precis Measurement Technol & Instru Tianjin Peoples R China

This paper proposes a robust method based on a local intensity binary pattern for interest feature description. To avoid estimating reference orientation, local features were calculated on a rotation invariant system. Different from the local binary patterns (LBP) and center-symmetric-LBP operator, our proposed local circular contrast pattern (LCCP) operator calculates a local binary feature by comparing the relative intensity order information of each two adjacent elements in the sequence consisting of the sampling point and its neighboring points. To evaluate the performance of our proposed descriptor LCCP and other existing descriptors (e.g., scale-invariant feature transform, DAISY, HRI-CSLTP, multisupport region order-based gradient histogram-single, local intensity order pattern), image matching experiments were first conducted on the Oxford dataset, additional image pairs with complex light changes, image sequences with different noise, and three-dimensional objects dataset. To further evaluate the discriminative ability of local descriptors, a simple object recognition experiment was carried out on three public datasets. The experimental results show that our descriptor LCCP exhibits a better performance and robustness than other evaluated descriptors. (C) 2017 SPIE and IS&T

关键词： binary pattern reference orientation local binary patterns image matching scale invariant feature transform

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Increasing anti-spoofing protection in speaker verification using linear prediction

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第6期76卷 9017-9032页

作者： Janicki, Artur Warsaw Univ Technol Div Cybersecur Inst Telecommun Nowowiejska 15-19 PL-00665 Warsaw Poland

This article addresses the problem of anti-spoofing protection in an automatic speaker verification (ASV) system. An improved version of a previously proposed spoofing countermeasure is presented. The presented method is based on the analysis of linear prediction error that results from both short-and long-term prediction of the input speech signal. It was observed that non-natural speech signals, i.e., synthetic or converted speech, were predicted in a different way than genuine speech. Therefore, in contrast to the classical linear prediction analysis, where usually only the prediction coefficients are analyzed, in the proposed approach the residual (error) signals were examined. During this analysis, 23 various prediction parameters were extracted, such as the energy of the prediction error, prediction gains and temporal parameters related to the prediction error signals. Various binary classifiers were researched to separate human and spoof classes, however the support vector machines with radial basis function (SVM-RBF) yielded the best results. When tested on the corpora provided for the ASVspoof 2015 Challenge, the proposed countermeasure returned better results than the previous version of the algorithm and, in most of the cases, the baseline spoofing detector based on the local binary patterns (LBP). It is hoped that the proposed method can be part of a generalized spoofing countermeasure helping to increase security of ASV systems.

关键词： Speaker verification Spoofing Linear prediction local binary patterns SVM-RBF

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