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Spectral-spatial classification of hyperspectral data using spectral-domain local binary patterns

MULTIMEDIA TOOLS AND APPLICATIONS 2018年第22期77卷 29889-29903页

作者： Wang, Cai-ling Ren, Jinchang Wang, Hong-wei Zhang, Yinyong Wen, Jia Xian Shiyou Univ Sch Comp Sci Xian 710065 Shaanxi Peoples R China Univ Strathclyde Dept Elect & Elect Engn Glasgow Lanark Scotland Engn Univ CAPF Xian 710086 Shaanxi Peoples R China Tianjin Polytech Univ Sch Elect Engn Tianjin 300387 Peoples R China

It is of great interest in spectral-spatial features classification for hyperspectral images (HSI) with high spatial resolution. This paper presents a novel Spectral-spatial classification method for improving hyperspectral image classification accuracy. Specifically, a new texture feature extraction algorithm exploits spatial texture feature from spectrum is proposed. It employs local binary patterns (LBPs) in order to extract the image texture feature with respect to spectrum information diversity (SID) to measure the differences of spectrum information. The classifier adopted in this work is support vector machine (SVM) because of its outstanding classification performances. In this paper, two real hyperspectral image datasets are used for testing the performance of the proposed method. Our experimental results from real hyperspectral images indicate that the proposed framework can enhance the classification accuracy compare to traditional alternatives.

关键词： Hyperspectral image classification Spectral-spatial analysis local binary patterns Spectrum information diversity Support vector machine

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Ground-based cloud classification by learning stable local binary patterns

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ATMOSPHERIC RESEARCH 2018年 207卷 74-89页

作者： Wang, Yu Shi, Cunzhao Wang, Chunheng Xiao, Baihua Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100190 Peoples R China Shanxi Univ Sch Software Taiyuan 030006 Shanxi Peoples R China

Feature selection and extraction is the first step in implementing pattern classification. The same is true for ground-based cloud classification. Histogram features based on local binary patterns (LBPs) are widely used to classify texture images. However, the conventional uniform LBP approach cannot capture all the dominant patterns in cloud texture images, thereby resulting in low classification performance. In this study, a robust feature extraction method by learning stable LBPs is proposed based on the averaged ranks of the occurrence frequencies of all rotation invariant patterns defined in the LBPs of cloud images. The proposed method is validated with a ground-based cloud classification database comprising five cloud types. Experimental results demonstrate that the proposed method achieves significantly higher classification accuracy than the uniform LBP, local texture patterns (LTP), dominant LBP (DLBP), completed LBP (CLTP) and salient LBP (SaLBP) methods in this cloud image database and under different noise conditions. And the performance of the proposed method is comparable with that of the popular deep convolutional neural network (DCNN) method, but with less computation complexity. Furthermore, the proposed method also achieves superior performance on an independent test data set.

关键词： local binary patterns Cloud classification Feature selection and extraction Texture image

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Application of local binary patterns and cascade AdaBoost classifier for mice behavioural patterns detection and analysis

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Procedia Computer Science 2019年 159卷 1375-1386页

作者： Tobechukwu Agbele Blessing Ojeme Richard Jiang CAIR Department of Computer Science University of Cape Town Rondebosh 7701 and CAIR South Africa

The paper describes the application of local binary patterns and cascade AdaBoost classifier (CAC) to detect and analyse mice behavioural movement. This was done with a view to investigating the inconsistencies associated with current practices, whereby mice behavioural classification is achieved by means of human-generated labels. The developed cascade AdaBoost algorithm was able to detect eight different mice movement, and we develop a system that allows mice behavioural analysis in videos, with minimal supervision. Evaluating the results on Completeness, Consistency and Correctness, and based on the devised analysis, a solution was deployed, showing that machine learning plays an important role in translating video data into scientific knowledge. This is a useful addition to the animal behaviourist’s analytical toolkit.

关键词： computer vission machine learning mice behavioural pattern detection local binary patterns cascade AdaBoost classifier

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Comparative Study on local binary patterns for Mammographic Density and Risk Scoring

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JOURNAL OF IMAGING 2019年第2期5卷 24-24页

作者： George, Minu Zwiggelaar, Reyer Aberystwyth Univ Dept Comp Sci Aberystwyth SY23 3DB Dyfed Wales

Breast density is considered to be one of the major risk factors in developing breast cancer. High breast density can also affect the accuracy of mammographic abnormality detection due to the breast tissue characteristics and patterns. We reviewed variants of local binary pattern descriptors to classify breast tissue which are widely used as texture descriptors for local feature extraction. In our study, we compared the classification results for the variants of local binary patterns such as classic LBP (local binary Pattern), ELBP (Elliptical local binary Pattern), Uniform ELBP, LDP (local Directional Pattern) and M-ELBP (Mean-ELBP). A wider comparison with alternative texture analysis techniques was studied to investigate the potential of LBP variants in density classification. In addition, we investigated the effect on classification when using descriptors for the fibroglandular disk region and the whole breast region. We also studied the effect of the Region-of-Interest (ROI) size and location, the descriptor size, and the choice of classifier. The classification results were evaluated based on the MIAS database using a ten-run ten-fold cross validation approach. The experimental results showed that the Elliptical local binary Pattern descriptors and local Directional patterns extracted most relevant features for mammographic tissue classification indicating the relevance of directional filters. Similarly, the study showed that classification of features from ROIs of the fibroglandular disk region performed better than classification based on the whole breast region.

关键词： breast density classification risk estimation local binary patterns texture descriptors

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Gender Recognition with Uniform local binary patterns 26

Gender Recognition with Uniform Local Binary Patterns

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26th IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Cerkezi, Llukman Topal, Cihan Anadolu Univ Elekt Elekt Muhendisligi Bolumu Eskisehir Turkey

ISBN: (纸本)9781538615010

Automatic gender recognition has been applied in many areas such as analyzing the structure of demographic social environments. In this paper we present a gender recognition algorithm based on uniform local binary pattern (LBP). LBP has proven to be very efficient image descriptor in many pattern recognition applications. In feature extraction step of the proposed method, a well known variant of LBP, uniform local binary pattern is utilized. Before applying LBP the face detection and alignment tasks are performed respectively. By doing alignment, the overlapping of similar part of face has been achieved. Then, we combine local uniform LBP histograms to create the final feature vector. We show that in the experiments carried out in Feret face dataset that the proposed method can achieve up to 93.57% recognition rate.

关键词： face local binary patterns gender recognition

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The Effects of Image Pre- and Post-Processing, Wavelet Decomposition, and local binary patterns on U-Nets for Skin Lesion Segmentation

The Effects of Image Pre- and Post-Processing, Wavelet Decom...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ross-Howe, Sara Tizhoosh, H. R. Univ Waterloo Kimia Lab Waterloo ON Canada

ISBN: (纸本)9781509060146

Skin cancer is a widespread, global, and potentially deadly disease, which over the last three decades has afflicted more lives in the USA than all other forms of cancer combined. There have been a lot of promising recent works utilizing deep network architectures, such as FCNs, U-Nets, and ResNets, for developing automated skin lesion segmentation. This paper investigates various pre- and post-processing techniques for improving the performance of U-Nets as measured by the Jaccard Index. The dataset provided as part of the "2017 ISBI Challenges on Skin Lesion Analysis Towards Melanoma Detection" was used for this evaluation and the performance of the finalist competitors was the standard for comparison. The pre-processing techniques employed in the proposed system included contrast enhancement, artifact removal, and vignette correction. More advanced image transformations, such as local binary patterns and wavelet decomposition, were also employed to augment the raw grayscale images used as network input features. While the performance of the proposed system fell short of the winners of the challenge, it was determined that using wavelet decomposition as an early transformation step improved the overall performance of the system over pre- and post-processing steps alone.

关键词： Skin Lesion Segmentation Deep Learning U-Nets Wavelets local binary patterns

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Comparative Analysis of Emotion Detection from Facial Expressions and Voice Using local binary patterns and Markov Models 2018

Comparative Analysis of Emotion Detection from Facial Expres...

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2nd International Conference on Vision, Image and Signal Processing (ICVISP)

作者： Chengeta, Kennedy Univ KwaZulu Natal Sch Comp Sci & Math Westville Campus Durban South Africa

ISBN: (纸本)9781450365291

Emotion detection has been achieved widely in facial and voice recognition separately with considerable success. The 6 emotional categories coming out of the classification include anger, fear, disgust, happiness and surprise. These can be infered from one's facial expressions both in the form of micro and macro expressions. In facial expressions the emotions are derived by feature extracting the facial expressions in different facial poses and classifying the expression feature vectors derived. Similarly automatic classification of a person's speech's affective state has also been used in signal processing to give insights into the nature of emotions. Speech being a critical tool for communication has been used to derive the emotional state of a human being. Different approaches have been successfully used to derive emotional states either in the form of facial expression recognition or speech emotional recognition being used. Less work has looked at fusing the two approaches to see if this improves emotional recognition accuracy. The study analyses the strengths of both and also limitations of either. The study reveals that emotional derivation based on facial expression recognition and acoustic information complement each other and a fusion of the two leads to better performance and results compared to the audio or acoustic recognition alone.

关键词： Berlin Emotional Database local binary patterns Facial Recognition

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Multiplicative local binary patterns (MuLBP)

Multiplicative Local Binary Patterns (MuLBP)

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International Conference on Pattern Recognition Systems

作者： Marco Mora Marcelo Silva-Ibarra Monica Acevedo-Letelier Department of Computer Science Universidad Católica del Maule Chile Laboratory of Technological Research in Pattern Recognition Universidad Católica del Maule Chile Laboratory of Technological Research in Pattern Recognition Universidad Católica del Maule Chile

ISBN: (数字)9781839531088

Speckle is a multiplicative noise that greatly deteriorates images. In this paper a model of local binary patterns (LBP) adapted to images with speckle (MuLBP) is proposed. The multiplicative model is constructed by substituting the additive comparisons of the traditional LBP for multiplicative comparisons from the Bigeometric Calculus. The experiments were carried out considering the 10.824 images of the KTH-TIPS2, FMD, CASIA and UFI databases. To compare the additive and multiplicative models, the Euclidean distance between the LBP histograms of the image with noise and without noise is adopted. The results indicate that, the distance between the histograms, the image with noise with respect to the image without noise, is smaller for the multiplicative models than for the traditional additive models. The above means that, the multiplicative LBP represent in a better way the textures in images contaminated with speckle.

关键词： local binary patterns Speckle Bi-geometric calculus

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Statistical classifiers on local binary patterns for optical diagnosis of diabetic retinopathy 6

Statistical classifiers on local binary patterns for optical...

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Conference on Biophotonics - Photonic Solutions for Better Health Care VI

作者： Mukhopadhyay, Sabyasachi Pratiher, Sawon Mukherjee, Sukanya Pasupuleti, Gautham Barman, Ritwik Chhablani, Jay Panigrahi, Prasanta K. Indian Inst Sci Educ & Res Kolkata Mohanpur 741246 India Indian Inst Technol Kharagpur Kharagpur 721302 W Bengal India Univ Engn & Management Kolkata 700156 India Biodesign Innovat Labs Private Ltd Bengaluru 560029 India LV Prasad Eye Inst Kallam Anji Reddy CampusBanjara Hills Hyderabad 500034 India

ISBN: (数字)9781510618978

ISBN: (纸本)9781510618978

Diabetic retinopathy damages retina due to diabetes mellitus which leads to blindness. Here, we have applied local binary pattern (LBP) in order to capture the spatial variations of the refractive indices due to progress of diabetic retinopathy among retinal tissues. After extraction of discriminative textures as binary numbers, state of art machine learning algorithms like decision tree and K-NN have been applied to get the optimum detection accuracy in multiclass classifications of in vivo diabetic retinopathy images. Here it is quite apparent that K-NN provides better accuracy and specificity than decision tree.

关键词： local binary patterns Diabetic Retinopathy Decision Tree K-NN

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Using local binary patterns in Speckle Image Analysis 41

Using Local Binary Patterns in Speckle Image Analysis

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41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

作者： Bento, L. Tavora, L. Assuncao, P. Faria, S. Fonseca-Pinto, R. Inst Telecomunicacoes Multimedia Signal Proc Leiria Portugal Polytech Inst Leiria Leiria Portugal

ISBN: (纸本)9789532330953

Firstly described by Newton in the 17th century, speckle is an optical phenomenon which can be translated into image patterns produced by wave interferences of diffused reflections. In fact, the speckle pattern is generated by the multiple interference phenomena that occur when a rough surface is illuminated with a coherent source of light, producing randomly distributed reflected waves of the same frequency but different phases and amplitudes. Although it has been known for a long time, capturing video sequences of speckle patterns was dependent on recent technological developments, in particular, related to laser technology and microsensors. The speckle acquisition setup comprises a light source, usually a laser, an optical beam expander and a CCD camera. The generated interference patterns are captured in series of video sequences, to further be processed. In previous works, several image processing algorithms have been applied to analyze video frames of speckle, aimed to capture the evolution patterns in dynamic processes. However, due to the typical high frequencies of the changing patterns, classical texture algorithms mostly fail this goal. In this work, speckle dynamics are evaluated using local binary patterns (LBP) jointly with some of its main variants and a newly proposed algorithm, in a reactive hyperemia controlled test. The proposed methodology goes beyond the traditional implementations of LBPs, by considering an additional Gaussian filtering, a methodology thus coined as LBPg. The results, on one hand, confirm that the classical formulations of LBP are not sensitive to changes in the simulated patterns but, on the other hand, demonstrate that the newly proposed LBP-adapted algorithm successfully identify the dynamics of the processes under study.

关键词： local binary patterns Laser Speckle Video Processing Computer Vision

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