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Texture descriptors based on adaptive neighborhoods for classification of pigmented skin lesions

JOURNAL OF ELECTRONIC IMAGING 2015年第6期24卷 061104-061104页

作者： Gonzalez-Castro, Vctor Debayle, Johan Wazaefi, Yanal Rahim, Mehdi Gaudy-Marqueste, Caroline Grob, Jean-Jacques Fertil, Bernard Univ Edinburgh Neuroimaging Sci Ctr Clin Brain Sci Edinburgh EH6 8SP Midlothian Scotland Ecole Natl Super Mines SPIN LGF UMR CNRS 5307 F-42023 St Etienne France CNRS UMR 7296 Lab Sci Informat & Syst F-13288 Marseille 9 France Hop Enfants La Timone Serv Dermatol F-13385 Marseille 5 France

Different texture descriptors are proposed for the automatic classification of skin lesions from dermoscopic images. They are based on color texture analysis obtained from (1) color mathematical morphology (MM) and Kohonen self-organizing maps (SOMs) or (2) local binary patterns (LBPs), computed with the use of local adaptive neighborhoods of the image. Neither of these two approaches needs a previous segmentation process. In the first proposed descriptor, the adaptive neighborhoods are used as structuring elements to carry out adaptive MM operations which are further combined by using Kohonen SOM;this has been compared with a non-adaptive version. In the second one, the adaptive neighborhoods enable geometrical feature maps to be defined, from which LBP histograms are computed. This has also been compared with a classical LBP approach. A receiver operating characteristics analysis of the experimental results shows that the adaptive neighborhood-based LBP approach yields the best results. It outperforms the nonadaptive versions of the proposed descriptors and the dermatologists' visual predictions. (C) 2015 SPIE and IS&T

关键词： general adaptive neighborhoods local binary patterns mathematical morphology self-organizing maps texture description

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Power LBP: A Novel Texture Operator for Smiling and Neutral Facial Display Classification

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Procedia Computer Science 2015年 51卷 1555-1564页

作者： Bogdan Smolka Karolina Nurzynska Institute of Automatic Control Silesian University of Technology ul. Akademicka 16 44-100 Gliwice Poland Institute of Informatics Silesian University of Technology ul. Akademicka 16 44-100 Gliwice Poland

Texture operators are commonly used to describe image content for many purposes. Recently they found its application in the task of emotion recognition, especially using local binary patterns method, LBP. This paper introduces a novel texture operator called power LBP, which defines a new ordering schema based on absolute intensity differences. Its definition as well as interpretation are given. The performance of suggested solution is evaluated on the problem of smiling and neutral facial display recognition. In order to evaluate the power LBP operator accuracy, its discriminative capacity is compared to several members of the LBP family. Moreover, the influence of applied classification approach is also considered, by presenting results for k-nearest neighbour, support vector machine, and template matching classifiers. Furthermore, results for several databases are compared.

关键词： Power LBP local binary patterns Classification Emotion recognition

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OBJECT-BASED FEATURE EXTRACTION AND SEMI-SUPERVISED CLASSIFICATION FOR URBAN CHANGE DETECTION USING HIGH-RESOLUTION REMOTE SENSING IMAGES

OBJECT-BASED FEATURE EXTRACTION AND SEMI-SUPERVISED CLASSIFI...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Bin Hou Qingjie Liu Yunhong Wang State Key Laboratory of Virtual Reality Technology and Systems The School of Computer Science and Engineering Beihang University

ISBN: (纸本)9781479979301

This paper presents a novel approach for urban change detection of high resolution (HR) remote sensing images. To overcome deficiency of traditional pixel-based methods and better annotate HR images, object-based strategies are adopted. Firstly change vector analysis (CVA) and local binary patterns (LBP) are utilized to extract the object-specific features based on the image-objects acquired by multitemporal segmentation. Then sparse representation is further exploited to characterize highly effective sparse features. Finally, the final change map is obtained by support vector machine (SVM) with the pseudotraining set acquired by expectation maximization (EM). Comparative experiments demonstrate the effectiveness of the proposed method.

关键词： feature extraction geophysical image processing geophysical techniques image classification image segmentation remote sensing support vector machines CVA LBP SVM change vector analysis expectation maximization high-resolution remote sensing images local binary patterns multitemporal segmentation object-based feature extraction semisupervised classification support vector machine traditional pixel-based method urban change detection Dictionaries Feature extraction Image color analysis Image resolution Image segmentation Principal component analysis Remote sensing Object-based method high resolution semi-supervised sparse representation Support Vector Network Feature extraction image segmentation remote sensing image colour analysis Cherry virus A Stroke Cyclophosphamide/Doxorubicin/Vincristine sparse representation image resolution Dictionaries as Topic geophysical techniques LBP gene LBP object orientation

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Texture Analysis for Colorectal Tumour Biopsies Using Multispectral Imagery

Texture Analysis for Colorectal Tumour Biopsies Using Multis...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Rémy Peyret Ahmed Bouridane Somaya Ali Al-Maadeed Suchithra Kunhoth Fouad Khelif Faculty of Engineering and Environment Northumbria University Newcastle Upon Tyne UK the Department of Engineering Qatar University Doha Qatar

ISBN: (纸本)9781424492695

Colorectal cancer is one of the most common cancers in the world. As part of its diagnosis, a histological analysis is often run on biopsy samples. Multispecral imagery taken from cancer tissues can be useful to capture more meaningful features. However, the resulting data is usually very large having a large number of varying feature types. This papers aims to investigate and compare the performances of multispectral imagery taken from colorectal biopsies using different techniques for texture feature extraction inclduing local binary patterns, Haraclick features and local intensity order patterns. Various classifiers such as Support Vector Machine and Random Forest are also investigated. The results show the superiority of multispectral imaging over the classical panchromatic approach. In the multispectral imagery's analysis, the local binary patterns combined with Support Vector Machine classifier gives very good results achieving an accuracy of 91.3%.

关键词： multispectral imagery's analysis local binary patterns local binary patterns combined

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Improving the performance of machine learning algorithms using fuzzy-based features for medical x-ray image classification

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2014年第6期27卷 3169-3180页

作者： Ghofrani, Fatemeh Helfroush, Mohammad Sadegh Danyali, Habibollah Kazemi, Kamran Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran

This paper proposes a novel approach for medical x-ray image classification using fuzzification of Contourlet-based Center Symmetric local binary patterns (CCS-LBPs). The proposed classification method consists of three stages. In the first stage, local features are obtained by partitioning each image into 25 overlapping sub-images, computing the 2-level contourlet transform of each subimage and extracting CS-LBPs from each resulting subband. In the second stage, fuzzy logic using reduced CCS-LBPs is employed to determine the degree of membership of subimages to each class. Finally, in order to assign images to their respective classes, we utilize membership values as the input of classifiers such as support vector machine (SVM) and k-nearest neighbor (K-NN). This work makes a major contribution to improve the performance of these classifiers. We conducted experiments on a subset of IRMA dataset to evaluate the effectiveness of our classification scheme. Experimental results reveal that the proposed scheme not only achieves a very good performance but also learns well even with a small number of training images.

关键词： Medical x-ray images local binary patterns contourlet transform fuzzy membership image classification

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Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped binary patterns 15

Emotion Recognition in the Wild via Convolutional Neural Net...

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Proceedings of the 2015 ACM on International Conference on Multimodal Interaction

作者： Gil Levi Tal Hassner The Open University of Israel Raanana Israel USC/Information Sciences Institute & The Open University of Israel Los Angeles CA USA

ISBN: (纸本)9781450339124

We present a novel method for classifying emotions from static facial images. Our approach leverages on the recent success of Convolutional Neural Networks (CNN) on face recognition problems. Unlike the settings often assumed there, far less labeled data is typically available for training emotion classification systems. Our method is therefore designed with the goal of simplifying the problem domain by removing confounding factors from the input images, with an emphasis on image illumination variations. This, in an effort to reduce the amount of data required to effectively train deep CNN models. To this end, we propose novel transformations of image intensities to 3D spaces, designed to be invariant to monotonic photometric transformations. These are applied to CASIA Webface images which are then used to train an ensemble of multiple architecture CNNs on multiple representations. Each model is then fine-tuned with limited emotion labeled training data to obtain final classification models. Our method was tested on the Emotion Recognition in the Wild Challenge (EmotiW 2015), Static Facial Expression Recognition sub-challenge (SFEW) and shown to provide a substantial, 15.36% improvement over baseline results (40% gain in performance).

关键词： local binary patterns emotiw 2015 challenge emotion recognition deep learning

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Description of interest regions with local binary patterns

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PATTERN RECOGNITION 2009年第3期42卷 425-436页

作者： Heikkila, Marko Pietikainen, Matti Schmid, Cordelia Infotech Oulu Machine Vis Grp FI-90014 Oulu Finland Univ Oulu Elect & Informat Engn Dept FI-90014 Oulu Finland INRIA Grenoble F-38330 Montbonnot St Martin France

This paper presents a novel method for interest region description. We adopted the idea that the appearance of an interest region can be well characterized by the distribution of its local features. The most well-known descriptor built on this idea is the SIFT descriptor that uses gradient as the local feature. Thus far, existing texture features are not widely utilized in the context of region description. In this paper, we introduce a new texture feature called center-symmetric local binary pattern (CS-LBP) that is a modified version of the well-known local binary pattern (LBP) feature. To combine the strengths of the SIFT and LBP, we use the CS-LBP as the local feature in the SIFT algorithm. The resulting descriptor is called the CS-LBP descriptor. In the matching and object category classification experiments, our descriptor performs favorably compared to the SIFT. Furthermore, the CS-LBP descriptor is computationally simpler than the SIFT. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Region description Region detection local binary patterns SIFT Image matching Object recognition

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Face Recognition using Two-Dimensional Tunable-Q Wavelet Transform

Face Recognition using Two-Dimensional Tunable-Q Wavelet Tra...

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International Conference on Digital Image Computing: Techniques and Applications

作者： T. Sunil Kumar Vivek Kanhangad Electrical Engineering Indian Institute of Technology Indore

ISBN: (纸本)9781467367967

Tunable-Q wavelet transform (TQWT) is a discrete wavelet transform that has been very effective in decomposing signals with oscillatory nature. In this paper, we develop a new two dimensional tunable-Q wavelet transform (2D-TQWT) using its 1D prototype and propose an approach for face recognition using 2D-TQWT. The proposed approach decomposes a face image into four sub bands. This is followed by extraction of local binary pattern based histogram features from different subbands. This extracted information is further combined to get the final representation. In order to evaluate the performance of the proposed 2D-TQWT based face recognition approach, experiments are carried out on two datasets namely, Yale and ORL face datasets. The performance of proposed approach is also compared with other existing wavelets. Experimental results show that the 2D-TQWT yields better recognition accuracy than other wavelets employed in our experiments for comparison.

关键词： Tunable-Q wavelet transform Face recognition local binary patterns NN-classifier

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Extended Gabor approach applied to classification of emphysematous patterns in computed tomography

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2014年第4期52卷 393-403页

作者： Nava, Rodrigo Escalante-Ramirez, Boris Cristobal, Gabriel Estepar, Raul San Jose Univ Nacl Autonoma Mexico Mexico City 04510 DF Mexico Univ Nacl Autonoma Mexico Fac Ingn Dept Procesamiento Senales Mexico City 04510 DF Mexico CSIC Inst Opt Madrid 28006 Spain Harvard Univ Brigham & Womens Hosp Sch Med Boston MA 02115 USA

Chronic obstructive pulmonary disease (COPD) is a progressive and irreversible lung condition typically related to emphysema. It hinders air from passing through airpaths and causes that alveolar sacs lose their elastic quality. Findings of COPD may be manifested in a variety of computed tomography (CT) studies. Nevertheless, visual assessment of CT images is time-consuming and depends on trained observers. Hence, a reliable computer-aided diagnosis system would be useful to reduce time and inter-evaluator variability. In this paper, we propose a new emphysema classification framework based on complex Gabor filters and local binary patterns. This approach simultaneously encodes global characteristics and local information to describe emphysema morphology in CT images. Kernel Fisher analysis was used to reduce dimensionality and to find the most discriminant nonlinear boundaries among classes. Finally, classification was performed using the k-nearest neighbor classifier. The results have shown the effectiveness of our approach for quantifying lesions due to emphysema and that the combination of descriptors yields to a better classification performance.

关键词： COPD Emphysema Gabor filters Kernel Fisher analysis local binary patterns Texture analysis

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Image Quality Assessment with Degradation on Spatial Structure

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IEEE SIGNAL PROCESSING LETTERS 2014年第4期21卷 437-440页

作者： Wu, Jinjian Lin, Weisi Shi, Guangming Xidian Univ Sch Elect Engn Key Lab Intelligent Percept & Image Understanding Minist Educ China Xian Shaanxi Peoples R China Nanyang Technol Univ Sch Comp Engn Nanyang 639798 Singapore

In this letter, we introduce an improved structural degradation based image quality assessment (IQA) method. Most of the existing structural similarity based IQA metrics mainly consider the spatial contrast degradation but have not fully considered the changes on the spatial distribution of structures. Since the human visual system (HVS) is sensitive to degradations on both spatial contrast and spatial distribution, both factors need to be considered for IQA. In order to measure the structural degradation on spatial distribution, the local binary patterns (LBPs) are first employed to extract structural information. And then, the LBP shift between the reference and distorted images is computed, because noise distorts structural patterns. Finally, the spatial contrast degradation on each pair of LBP shifts is calculated for quality assessment. Experimental results on three large benchmark databases confirm that the proposed IQA method is highly consistent with the subjective perception.

关键词： Image quality assessment local binary patterns spatial distribution structural degradation

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