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Hyperspectral image classification by combining local binary pattern and joint sparse representation

INTERNATIONAL JOURNAL OF REMOTE SENSING 2019年第24期40卷 9484-9500页

作者： Tu, Bing Kuang, Wenlan Zhao, Guangzhe He, Danbing Liao, Zhuolang Ma, Weiwen Hunan Inst Sci & Technol Sch Informat Sci & Engn Yueyang Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha Hunan Peoples R China Beijing Univ Civil Engn & Architecture Coll Elect & Informat Engn Beijing Peoples R China

This paper proposes a new hyperspectral classification method that combines the joint sparse representation classifier (JSRC) and local binary pattern (LBP) (JSR-LBP). The proposed method uses a statistical histogram of the LBP feature spectrum as a feature vector for classification and recognition. Using the LBP method let us extract the local texture feature of an image more accurately and effectively because of the forceful robustness to light. The proposed method JSR-LBP mainly includes the following steps: First, the JSRC is used to obtain the representation residuals of different pixels. Then, the LBP value is calculated for every pixel in the whole image to generate the LBP histogram;the LBP histogram of the test sample can be obtained from a series of binary codes that are generated using statistics. Next, the Bhattacharyya coefficient is employed to measure the similarity between the test and training samples unlike the traditional classifiers as -nearest neighbor that usually employ the Euclidean distance as similarity metric, and a regularization parameter is then introduced to achieve the balance between the JSRC and LBP. Finally, the test pixel's label is determined by applying the final residual. Experimental results performed on three real hyperspectral images data demonstrate the outstanding performance of the proposed approach compared to other broader classifiers.

关键词： local binary pattern

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Perceptual Image Hashing Based on Weber local binary pattern and Color Angle Representation

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IEEE ACCESS 2019年 7卷 45460-45471页

作者： Qin, Chuan Hu, Yecen Yao, Heng Duan, Xintao Gao, Liping Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China Fudan Univ Shanghai Key Lab Data Sci Shanghai 200433 Peoples R China Henan Normal Univ Coll Comp & Informat Engn Xinxiang 453007 Henan Peoples R China

This paper proposes an efficient scheme for generating image hashing by combining the local texture and color angle features. During the stage of texture extraction, using Weber's Law, the difference ratios between the center pixels and their surrounding pixels are calculated and the dimensions of these values are further reduced by applying principal component analysis to the statistical histogram. In the stage of color feature extraction, the color angle of each pixel is computed before dimensional reduction and is carried out using a discrete cosine transform and a significant coefficients selection strategy. The main contribution of this paper is a novel construction for image hashing that incorporates texture and color features by using Weber local binary pattern and color angular pattern. The experimental results demonstrate the efficacy of the proposed scheme, especially for the perceptual robustness against common contentpreserving manipulations, such as the JPEG compression, Gaussian low-pass filtering, and image scaling. Based on the comparisons with the state-of-the-art schemes, receiver operating characteristic curves and integrated histograms of normalized distances show the superiority of our scheme in terms of robustness and discrimination.

关键词： Image hashing Weber's law local binary pattern color angular pattern

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A Novel Face Recognition Algorithm based on the Deep Convolution Neural Network and Key Points Detection Jointed local binary pattern Methodology

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2017年第1期12卷 363-372页

作者： Huang, Wen-zhun Zhang, Shan-wen Xijing Univ Sch Informat Engn Xian Peoples R China

This paper presents a novel face recognition algorithm based on the deep convolution neural network and key point detection jointed local binary pattern methodology to enhance the accuracy of face recognition. We firstly propose the modified face key feature point location detection method to enhance the traditional localization algorithm to better pre-process the original face images. We put forward the grey information and the color information with combination of a composite model of local information. Then, we optimize the multi-layer network structure deep learning algorithm using the Fisher criterion as reference to adjust the network structure more accurately. Furthermore, we modify the local binary pattern texture description operator and combine it with the neural network to overcome drawbacks that deep neural network could not learn to face image and the local characteristics. Simulation results demonstrate that the proposed algorithm obtains stronger robustness and feasibility compared with the other state-of-the-art algorithms. The proposed algorithm also provides the novel paradigm for the application of deep learning in the field of face recognition which sets the milestone for further research.

关键词： Face recognition Deep convolution neural network Key points detection local binary pattern Feature extraction Optimization

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Environmental Sound Classification Using local binary pattern and Audio Features Collaboration

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IEEE TRANSACTIONS ON MULTIMEDIA 2021年 23卷 3978-3985页

作者： Toffa, Ohini Kafui Mignotte, Max Univ Montreal Fac Arts & Sci Vis Lab Dept Informat & Rech Operat DIRO Montreal PQ H3C 3J7 Canada

This paper presents a new approach to classify environmental sounds using a texture feature local binary pattern (LBP) and audio features collaboration. To our knowledge, this is the first time that the LBP (or its variants), which has a proven track record in the field of image recognition and classification, has been generalized for 1D and combined with audio features for an environmental sound classification task. To this end, we have generalized and defined LBP-1D and local phase quantization (LPQ)-1D on the 1-dimensional (1D) audio signal and have applied the original LBP, the variance LBP (VARLBP) and the extended LBP (ELBP) thus generated to the spectrogram of the audio signal in order to model the sound texture. We have also extensively compared these new LBP-based features to the classical audio descriptors commonly used in environmental sound classification, such as MFCC, GFCC, CQT, chromagram, STE and ZCR. We have evaluated our algorithm on ESC-10 and ESC-50 datasets using classicalmachine learning algorithms, such as support vector machines (SVM), random forest and k-nearest neighbor (kNN). The results showed that the LBP features outperform the classical audio features. We mix theLBPfeatures with the audio descriptors, and our best mixed model achieves state-of-the-art results for environmental sound classification: 88.5% on ESC-10 and 64.6% on ESC-50. Those results outperform the results of methods that used handcrafted features with classical machine learning algorithms and are similar to some convolutional neural networkbased methods. Although our method is not the cutting edge of the state-of-the-art methods, it is faster than any convolutional neural networkmethods and represents a better choice when there is data scarcity or minimal computing power.

关键词： Environmental sound classification local binary pattern local phase quantization machine learning ESC-50 audio signal spectrogram SVM random forest kNN

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Rotation and scale invariant local binary pattern based on high order directional derivatives for texture classification

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DIGITAL SIGNAL PROCESSING 2014年第1期26卷 142-152页

作者： Yuan, Feiniu Jiangxi Univ Finance & Econ Sch Informat Technol Nanchang 330032 Jiangxi Peoples R China

local binary pattern (LBP) only encodes the first order directional derivatives of a center pixel but it does not consider higher order derivatives. This paper proposes a rotation and scale invariant local binary pattern by jointly taking into account high order directional derivatives, circular shift sub-uniform, and scale space. Each order directional derivatives are independently encoded in a similar way of the first order derivatives to generate a code for the center pixel. Different order derivatives produce different codes that result in several histograms over an image, and then all the histograms multiplied by weights are concatenated together to fully utilize information of different order derivatives. To further improve performance, circular shift sub-uniform and scale space techniques are used to obtain rotation and scale invariant local binary patterns. Extensive experiments show that the high order derivatives based LBP can achieve good performance and obviously outperforms existing methods. (C) 2013 Elsevier Inc. All rights reserved.

关键词： local binary pattern Directional derivatives High order Scale space

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*** pyramid transform and local binary pattern based robust face recognition for e-health secured login

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COMPUTERS & ELECTRICAL ENGINEERING 2016年 53卷 435-443页

作者： Alelaiwi, Abdulhameed Abdul, Wadood Dewan, M. Solaiman Migdadi, Mahmoud Muhammad, Ghulam King Saud Univ CCIS Dept Software Engn Riyadh Saudi Arabia King Saud Univ Dept Comp Engn CCIS Riyadh Saudi Arabia Minist Women & Children Affair Sylhet Bangladesh PSUT Management Informat Syst Dept MIS Amman Jordan

This paper proposes a face recognition system based on a steerable pyramid transform (SPT) and local binary pattern (LBP) for e-Health secured login. In an e-Health framework, patients are sometimes unable to identify themselves by traditional login modalities such as username and password. Automatic face recognition can replace the conventional login modalities if the recognition system is robust. In the proposed system, SPT can decompose a face image into several subbands of different scales and orientations, and LBP can encode the subbands in binary texture pattern. Therefore, SPT-LBP scheme represents a face image in a robust way that includes multiple information sources from different scales and orientations. The proposed system is evaluated on the facial recognition technology (FERET) database. According to the results, the proposed system achieves 99.28% recognition in fb set, 80.17% in dup I set, and 79.54% in dup II set. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Steerable pyramid transform local binary pattern e-Health Face recognition

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WLBP: Weber local binary pattern for local image description

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NEUROCOMPUTING 2013年 120卷 325-335页

作者： Liu, Fan Tang, Zhenmin Tang, Jinhui Nanjing Univ Sci & Technol Sch Comp Sci & Technol Nanjing 210094 Jiangsu Peoples R China

In this paper, we propose a local descriptor, called Weber local binary pattern (WLBP1), which effectively combines the advantages of WLD and LBP. Specifically, WLBP consists of two components: differential excitation and LBP. The differential excitation extracts perception features by Weber's law, while the LBP (local binary pattern) can describe local features splendidly. By computing the two components, we obtain wo images: differential excitation image and LBP image, from which a WLBP histogram is constructed. The differential excitation was extended by bringing in Laplacian of Gaussian (LoG), which makes WLBP robust to noise. By designing a new quantization method, the discriminabilty of WLBP was enhanced. The proposed method is evaluated on the face recognition problem under different challenges. Experimental results show that WLBP performs better than WLD and LBP. Meanwhile, it is robust to time, facial expressions, lightings, pose and noise. We also conduct experiments on Brodatz and KTH-TIPS2-a texture databases, which demonstrate that WLBP is a powerful texture descriptor. (c) 2013 Elsevier B.V. All rights reserved.

关键词： local binary pattern Weber's law Differential excitation Face recognition Texture classification

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Improved color texture recognition using multi-channel orthogonal moments and local binary pattern

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第9期80卷 13179-13194页

作者： Hosny, Khalid M. Magdy, Taher Lashin, Nabil A. Zagazig Univ Informat Technol Dept Zagazig 44519 Egypt Sinai Univ Comp Sci Dept Arish North Sinai Egypt

The texture is an essential characteristic of the image. So, recognition of texture is increasingly becoming a major topic in many image processing applications such as image retrieving, image classification, similarity, object recognition, and detection. The recognition of texture tries to allocate an unidentified image to one of the identified class of textures. This paper proposes a novel feature extraction technique for classification and recognition of color texture. The significant advantage of the introduced method is that it combines the extraction of local and global features of the color texture by using local binary pattern (LBP) and multi-channel orthogonal radial substituted Chebyshev moments, respectively. Relevant features (local or global) provides discriminatory information that used to differentiate one object from another. Global features represent the image as a whole, while local features represent a specific part of the image. We performed experiments using challenging datasets: (Outex, ALOT) to test the efficacy of our image classification descriptors. The result of this approach has said that our descriptor is valid, competitive, discriminatory, and exceeds the current state-of-art methods.

关键词： Color textures Classification Multi-Channel orthogonal descriptors local binary pattern

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Empirical mode decomposition and local binary pattern based feature extraction for face recognition

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IMAGING SCIENCE JOURNAL 2024年第6期72卷 791-807页

作者： Shoba, V. Betcy Thanga Sam, I. Shatheesh Manonmaniam Sundaranar Univ Nesamony Mem Christian Coll Dept PG Comp Sci Tirunelveli India ManonmaniamSundaranar Univ Nesamony Mem Christian Coll Dept PG Comp Sci Tirunelveli 627012 Tamil Nadu India

Facial recognition is a challenging pattern recognition problem in computer vision. This paper proposes a face recognition system that uses Empirical Mode Decomposition (EMD) and local binary pattern (LBP) based feature extraction for a robust face recognition system. This scheme initially decomposes the image into 2N number of IMF (Intrinsic Mode Function) images, where N numbers of IMF images are estimated in the X direction and N number of IMF images are estimated in the Y direction. From the 2N number of IMFs, the M number of best matching IMF image pairs is estimated using 2D Discrete Fourier Transform (DFT). The IMF pair is added to extract the IC-LBP (Intensity Compensated-local binary pattern) features. The IC-LBP features are extracted from the IMF images such that the center intensity is adjusted based on an adaptive intensity threshold. The use of EMD and IC-LBP features uses the essential descriptors that best represent a facial image. The same process is repeated in the testing phase where the test image is categorized from the trained images using the Naive Bayes algorithm. The performance evaluation was done using the Yale, MORPH, and FGNET using metrics such as time complexity and recognition rate on different types of test face images like partial faces, different lightning, and rotation. Results show that the proposed face recognition system outperforms the traditional algorithms. Results show that the proposed face recognition system outperforms the traditional algorithms.

关键词： empirical mode decomposition local binary pattern naive bayes recognition rate time complexity intrinsic mode function face recognition discrete fourier transform

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Mean distance local binary pattern: a novel technique for color and texture image retrieval for liver ultrasound images

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第14期80卷 20773-20802页

作者： Bedi, Anterpreet Kaur Sunkaria, Ramesh Kumar Ambedkar Natl Inst Technol Dept Elect & Commun Engn Jalandhar India

A rapid growth in medical ultrasound database makes it difficult for medical practitioners to manage and search relevant data with good efficiency. Hence, a novel image retrieval technique using Mean Distance local binary pattern (Mean Distance LBP) has been proposed for content-based image retrieval. The conventional local binary pattern (LBP) converts every pixel of image into a binary pattern based on their relationship with neighbourhood pixels. The proposed feature descriptor differs from local binary pattern as it transforms the mutual relationship of all neighbouring pixels in a binary pattern based on their standard deviation templates as well as Euclidean distance from the center pixel. Color feature and Gray Level Co-occurrence Matrix have also been used in this work. To prove the excellence of the proposed method, experiments have been conducted on two different databases of natural images and face images. Further, the method is applied on real time ultrasound database for retrieval of liver images from a set of ultrasound images of various organs. The performance has been observed using well-known evaluation measures, precision and recall, and compared with some state-of-art local patterns. Comparison shows a significant improvement in the proposed method over existing methods.

关键词： local binary pattern Mean distance local binary pattern Content based image retrieval Gray level co-occurrence matrix Ultrasound database

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