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Perceptually relevant grouping of image tokens on the basis of constraint propagation from local binary patterns

JOURNAL OF ELECTRONIC IMAGING 2016年第6期25卷 1-9页

作者： Behlim, Sadaf Iqbal Syed, Tahir Qasim Malik, Muhammad Yameen Vigneron, Vincent Visual Analyt Lab Karachi Pakistan Natl Univ Comp & Emerging Sci Dept Comp Sci Sect 17D St 4Natl Highway Karachi 75030 Pakistan Univ Evry Val dEssonne 40 Rue Pelvoux F-91020 Evry France

Grouping image tokens is an intermediate step needed to arrive at meaningful image representation and summarization. Usually, perceptual cues, for instance, gestalt properties inform token grouping. However, they do not take into account structural continuities that could be derived from other tokens belonging to similar structures irrespective of their location. We propose an image representation that encodes structural constraints emerging from local binary patterns (LBP), which provides a long-distance measure of similarity but in a structurally connected way. Our representation provides a grouping of pixels or larger image tokens that is free of numeric similarity measures and could therefore be extended to nonmetric spaces. The representation lends itself nicely to ubiquitous image processing applications such as connected component labeling and segmentation. We test our proposed representation on the perceptual grouping or segmentation task on the popular Berkeley segmentation dataset (BSD500) that with respect to human segmented images achieves an average F-measure of 0.559. Our algorithm achieves a high average recall of 0.787 and is therefore well-suited to other applications such as object retrieval and category-independent object recognition. The proposed merging heuristic based on levels of singular tree component has shown promising results on the BSD500 dataset and currently ranks 12th among all benchmarked algorithms, but contrary to the others, it requires no data-driven training or specialized preprocessing. (C) 2016 SPIE and IS& T

关键词： image segmentation structural constraints propagation local binary patterns image representation learning

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Continuous Authentication Using One-Dimensional Multi-Resolution local binary patterns (1DMRLBP) in ECG Biometrics

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2016年第12期11卷 2818-2832页

作者： Louis, Wael Komeili, Majid Hatzinakos, Dimitrios Univ Toronto Edward S Rogers Sr Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada

The objective of a continuous authentication system is to continuously monitor the identity of subjects using biometric systems. In this paper, we proposed a novel feature extraction and a unique continuous authentication strategy and technique. We proposed One-Dimensional Multi-Resolution local binary patterns (1DMRLBP), an online feature extraction for one-dimensional signals. We also proposed a continuous authentication system, which uses sequential sampling and 1DMRLBP feature extraction. This system adaptively updates decision thresholds and sample size during run-time. Unlike most other local binary patterns variants, 1DMRLBP accounts for observations' temporal changes and has a mechanism to extract one feature vector that represents multiple observations. 1DMRLBP also accounts for quantization error, tolerates noise, and extracts local and global signal morphology. This paper examined electrocardiogram signals. When 1DMRLBP was applied on the University of Toronto database (UofTDB) 1,012 single session subjects database, an equal error rate (EER) of 7.89% was achieved in comparison to 12.30% from a state-of-the-art work. Also, an EER of 10.10% was resulted when 1DMRLBP was applied to UofTDB 82 multiple sessions database. Experiments showed that using 1DMRLBP improved EER by 15% when compared with a biometric system based on raw time-samples. Finally, when 1DMRLBP was implemented with sequential sampling to achieve a continuous authentication system, 0.39% false rejection rate and 1.57% false acceptance rate were achieved.

关键词： Biometrics continuous authentication pattern recognition electrocardiogram local binary patterns

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Medical image classification using spatial adjacent histogram based on adaptive local binary patterns

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COMPUTERS IN BIOLOGY AND MEDICINE 2016年 72卷 185-200页

作者： Liu, Dong Wang, Shengsheng Huang, Dezhi Deng, Gang Zeng, Fantao Chen, Huiling Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Wuhan Univ Renmin Hosp Dept Neurosurg Wuhan 430060 Peoples R China Wenzhou Univ Coll Phys & Elect Informat Engn Wenzhou 325035 Peoples R China

Medical image recognition is an important task in both computer vision and computational biology. In the field of medical image classification, representing an image based on local binary patterns (LBP) descriptor has become popular. However, most existing LBP-based methods encode the binary patterns in a fixed neighborhood radius and ignore the spatial relationships among local patterns. The ignoring of the spatial relationships in the LBP will cause a poor performance in the process of capturing discriminative features for complex samples, such as medical images obtained by microscope. To address this problem, in this paper we propose a novel method to improve local binary patterns by assigning an adaptive neighborhood radius for each pixel. Based on these adaptive local binary patterns, we further propose a spatial adjacent histogram strategy to encode the micro-structures for image representation. An extensive set of evaluations are performed on four medical datasets which show that the proposed method significantly improves standard LBP and compares favorably with several other prevailing approaches. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： local binary patterns Image classification Feature extraction Medical images Microscope images

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Land-use scene classification using multi-scale completed local binary patterns

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SIGNAL IMAGE AND VIDEO PROCESSING 2016年第4期10卷 745-752页

作者： Chen, Chen Zhang, Baochang Su, Hongjun Li, Wei Wang, Lu Univ Texas Dallas Dept Elect Engn Richardson TX 75083 USA Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Hohai Univ Sch Earth Sci & Engn Nanjing Jiangsu Peoples R China Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China

In this paper, we introduce the completed local binary patterns (CLBP) operator for the first time on remote sensing land-use scene classification. To further improve the representation power of CLBP, we propose a multi-scale CLBP (MS-CLBP) descriptor to characterize the dominant texture features in multiple resolutions. Two different kinds of implementations of MS-CLBP equipped with the kernel-based extreme learning machine are investigated and compared in terms of classification accuracy and computational complexity. The proposed approach is extensively tested on the 21-class land-use dataset and the 19-class satellite scene dataset showing a consistent increase on performance when compared to the state of the arts.

关键词： Land-use scene classification Multi-scale analysis local binary patterns Extreme learning machine

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Multi-quantized local binary patterns for facial gender classification

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COMPUTERS & ELECTRICAL ENGINEERING 2016年第0期54卷 271-284页

作者： Patel, Bhavik Maheshwari, R. P. Balasubramanian, R. Indian Inst Technol Roorkee Dept Elect Engn Roorkee 247667 Uttar Pradesh India Indian Inst Technol Roorkee Dept Comp Sci & Engn Roorkee 247667 Uttar Pradesh India

This paper presents multi-quantized local binary patterns for facial gender classification. For encoding the gray level difference (GLD) between a reference pixel and its neighbors, local binary pattern employs a binary quantization which retains the sign of GLD but discards the magnitude information. To improve the discrimination capability, the proposed method utilizes both the sign and magnitude components by performing multi-level vector quantization of GLD. Each quantized level is then separately encoded to generate multiple local binary patterns. The proposed method is evaluated on four publicly available datasets (FERET, PAL, CASIA and FEI) through extensive experiments. Comparison of performance with various existing methods clearly demonstrated that the proposed method has advantages such as higher discrimination power, improved noise robustness and better generalization capability. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Feature extraction Pattern classification local binary patterns Gender classification

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Data-driven techniques for smoothing histograms of local binary patterns

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PATTERN RECOGNITION 2016年 60卷 734-747页

作者： Ylioinas, Juha Poh, Norman Holappa, Jukka Pietikainen, Matti Univ Oulu Fac Informat Technol & Elect Engn Ctr Machine Vis & Signal Anal POB 4500 FI-90014 Oulu Finland Univ Surrey Fac Engn & Phys Sci Dept Comp Guildford GU2 7XH Surrey England

local binary pattern histograms have proved very successful texture descriptors. Despite this success, the description procedure bears some drawbacks that are still lacking solutions in the literature. One of the problems arises when the number of extractable local patterns reduces while their dimension increases rendering the output histogram descriptions sparse and unstable, finally showing up as a reduced recognition rate. A smoothing method based on kernel density estimation was recently proposed as a means to tackle the aforementioned problem. A constituent part of the method is to determine how much to smooth a histogram. Previously, this was solved via trial-and-error in a problem-specific manner. In this paper, the goal is to present data-driven methods to determine this smoothing automatically. In the end, we present unsupervised and supervised methods for the given task and validate their performance with a representative set of local binary pattern variants in texture analysis problems covering material categorization and face recognition. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： local binary patterns local binarized descriptors Histogram Soft-assignment Kernel density estimation Histogram smoothing

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Improving hand vein recognition by score weighted fusion of wavelet-domain multi-radius local binary patterns

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2016年第3期54卷 151-160页

作者： Wang, Yiding Duan, Qiangyu Shark, Lik-Kwan Huang, Di North China Univ Technol Sch Elect & Informat Engn Beijing 100144 Peoples R China Univ Cent Lancashire Sch Engn Appl Digital Signal & Image Proc Res Ctr Preston PR1 2HE Lancs England Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China

Among biometric modalities, hand vein patterns are seen as providing an attractive method for high-level security access applications owing to high impenetrability and good user convenience. For biometric recognition based on near-infrared dorsal hand vein images, local binary patterns (LBP) have emerged as a highly effective descriptor of local image texture with high recognition performance reported. In this paper, the traditional approach with LBP applied in the spatial domain is extended to multi-radius LBP in the wavelet domain to provide a more comprehensive set of feature categories to capture grey-level variation characteristics of vein patterns, and score weighted fusion based on the relative discriminative power of each feature category is proposed to achieve higher recognition performance. The proposed methodology is shown to provide a more robust performance with a recognition rate in excess of 99% and an equal error rate significantly less than 2%.

关键词： biometrics local binary patterns score weighted fusion hand vein recognition wavelet transform

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Labor Induction Failure Prediction Based on B-Mode Ultrasound Image Processing Using Multiscale local binary patterns

Labor Induction Failure Prediction Based on B-Mode Ultrasoun...

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International Conference on Optoelectronics and Image Processing (ICOIP)

作者： Pablo Vasquez, O. Arana, Nestor Izaguirre, Alberto Burgos, Jorge Mondragon Univ Arrasate Mondragon Spain BioCruces Hlth Res Inst Obstet & Gynecol Serv Bilbao Spain

ISBN: (纸本)9781509008803

Labor induction is defined as the artificial onset of labor for the purpose of vaginal birth. Cesarean section is one of the potential risks of labor induction as it occurs in about 20% of the inductions. A ripe cervix (soft and distensible) is needed for a successful labor. Changes occurring during the ripening process, will affect the interaction between cervical tissues and sound waves during ultrasound transvaginal scanning and will be perceived as gray level intensity variations in the echography image. Thus, a non-invasive method using image processing of ultrasound images may help in predicting the outcome of labor induction. In this paper a set of echography images from labor induction patients is analyzed using a multiscale methodology based on local binary patterns and circular Gabor filters. Results show that it is feasible to predict the outcome of a labor induction procedure using this method with a good accuracy.

关键词： component texture analysis cervix local binary patterns

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FUZZY THRESHOLD-BASED UNIFORM local binary patterns FOR HYPERSPECTRAL IMAGERY CLASSIFICATION 8

FUZZY THRESHOLD-BASED UNIFORM LOCAL BINARY PATTERNS FOR HYPE...

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8th Workshop on Hyperspectral Image and Signal Processing - Evolution in Remote Sensing (WHISPERS)

作者： Jia, Sen Hu, Jie Deng, Lin Su, Hongjun Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Hohai Univ Sch Earth Sci & Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781509006083

local binary patterns (LBP) is a simple and efficient texture description operator, and the improved "uniform" local binary patterns (uLBP) has been proved to be a powerful feature expression for texture classification. However, due to the "0" threshold operation in uLBP, it is sensitive to gray variance resulting from noises and disturbance among classes, which reduces the classification performance. In this paper, a fuzzy threshold-based uniform local binary patterns, abbreviated as ftuLBP, has been proposed to enhance the robustness of the descriptor to gray variance. Specifically, the proposed ftuLBP operator defines a slack variable sigma to allow the difference between the central pixel g(p) and neighbor pixel g(c) to vary in the interval (-sigma, sigma). If the result g(p)-g(c) is in the interval (-sigma, sigma), "0" is given. Otherwise, "1" is given. Through incorporating the fuzzy threshold operator, the ftuLBP method is more robust to noise and disturbance among classes (such as the mutual disturbance among pixels on the edge). The classification results using sparse representation-based classification (SRC) on the Pavia center hyperspectral data set have demonstrated the superiority of the proposed ftuLBP algorithm over the uLBP method.

关键词： Hyperspectral imagery classification local binary patterns fuzzy threshold operator

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A Face Recognition System Based on local binary patterns and Support Vector Machine for Home Security Service Robot 9

A Face Recognition System Based on Local Binary Patterns and...

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9th International Symposium on Computational Intelligence and Design (ISCID)

作者： Wang, Jiakailin Zheng, Jinjin Zhang, Shiwu He, Jijun Liang, Xiao Feng, Sui Univ Sci & Technol China Dept Precis Machinery & Precis Instrumentat Hefei Peoples R China

ISBN: (纸本)9781509035588

In this paper, we present a real-time face recognition system for home security service robot which can be applied to recognize the person's face in front and give a warning when the identity of the person is a stranger. Considering the complexity of the actual situation, there might be some errors causing by the following factors like the angle, the size, the environment and the illumination of the human face, which can hardly be avoided. Our system is designed to reduce and eliminate the influence of the factors above. This article first used local binary patterns (LBP) to detect face and cut out the face region. Then, based on the collected and pretreated face database, the target face could be recognized utilizing Support Vector Machine (SVM). However, the given face database are always irregular in size, position of the face area, scale and some other factors. The proposed face recognition system has been tested in practical application for home security service robot. The results indicate that our face recognition system is effective and real-time. It works well in multi-face recognition and stranger identification, which meet the demand of robot.

关键词： face recognition stranger identification home security service robot local binary patterns support vector machine

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