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Genetic-based feature fusion in face recognition using arithmetic coded local binary patterns

IET IMAGE PROCESSING 2020年第15期14卷 3742-3750页

作者： Najafi Khanbebin, Saeed Mehrdad, Vahid Lorestan Univ Dept Elect & Elect Khorramabad Iran

local binary patterns (LBPs) are one of the attempts for gathering local features with face recognition algorithms. Although the application of LBP's in many recognition contents is too apparent, these methods have limited accuracy because of their threshold value. One problem is earning one value for two different regions with a diverse pixel neighbourhood, which causes mistakes in feature vector and decreases the discriminative power. In this study, the authors proposed a modified LBP that covers the LBP's disadvantages. The proposed approach is arithmetic coded LBP (ACLBP) that uses arithmetic coding process during LBP calculation instead of applying original thresholds. The proposed policy addresses the problem of returning one similar LBP value for two different patches. Moreover, the proposed method modifies LBP by using a different threshold for calculating the pixels differences. Using this algorithm, the authors conducted a genetic-based feature fusion method by combining LBP and histogram of oriented gradients and ACLBP. The proposed approach could work better on LFW dataset, and the ORL dataset and Yale face dataset that shows the improving role of ACLBP in comparison with the earlier version of LBP.

关键词： image colour analysis feature extraction face recognition image classification image texture image fusion arithmetic codes image coding genetic algorithms gradient methods ACLBP local binary patterns local features face recognition algorithms recognition contents threshold value diverse pixel neighbourhood feature vector modified LBP LBP's disadvantages arithmetic coding process LBP calculation original thresholds similar LBP value different patches different threshold pixels differences fusion method

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Applying maximally stable extremal regions and local binary patterns for guide-wire detecting in percutaneous coronary intervention

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IET IMAGE PROCESSING 2019年第13期13卷 2579-2586页

作者： Pusit, Prasong Xie, Xiao-Liang Hou, Zeng-Guang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing 100190 Peoples R China

Intervention surgery strongly requires information on the guide-wire position under the monitoring of X-ray video. Hence, the related researches such as guide-wire detecting or tracking have become widespread. However, most of the existing methods require a lot of resources for computing or large data for training since the X-ray videos have internal physicals such as anatomical skeleton contours and organs that are quite similar to a guide-wire. This work presents a practical method that only requires a moderate number of training data for detecting a guide-wire tip in an X-ray video sequence during the percutaneous coronary intervention surgery. The method applies maximally stable extremal regions (MSER) combine with modified multi-filters (region area range filter and stroke width variation filter) for region detection and local binary patterns (LBP) for guide-wire recognition. The motivation for applying MSER and LBP are the robust efficacy and the low requirement of resources. The approach evaluated 20 different sequences of X-ray videos, a total of 1295 frames. 50 selected frames were used as training templates and others to experiment. The method was successfully performed to the detecting guide-wires with p-value < 0.01 compared with conventional MSER methods, 93.7% average detection accuracy, and 21 fps average speed.

关键词： blood vessels medical image processing surgery image sequences video signal processing image filtering object detection X-ray imaging object tracking stroke width variation filter region detection local binary patterns guide-wire recognition conventional MSER methods maximally stable extremal regions guide-wire position anatomical skeleton contours training data X-ray video sequence percutaneous coronary intervention surgery region area range filter X-ray video monitoring guide-wire tip detection modified multifilters training templates

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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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Multiresolution approach for multiple human detection using moments and local binary patterns

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MULTIMEDIA TOOLS AND APPLICATIONS 2015年第17期74卷 7037-7062页

作者： Nigam, Swati Khare, Ashish Univ Allahabad Dept Elect & Commun Allahabad 211002 Uttar Pradesh India

Human detection is a central problem in development of any surveillance application. In this study, we present a simple and efficient, multi-resolution gray scale invariant approach for multiple human detection. The multiresolution is important for objects of different size and gray scale invariance is important due to uneven illumination and within-class variability. The proposed method is based on integration of central moments upon multi-resolution gray scale invariant local binary patterns operator. Since, the local binary patterns operator is invariant against different resolutions of space scale and monotonic change in gray scale, therefore the proposed method is robust in terms of variations in space scale as well as gray scale. Another advantage is high computational accuracy of the method due to use of moment operator which enhances the efficiency of the proposed method. Moreover, the proposed method is simple, as these operations can be performed within a few steps in a small neighborhood and a lookup table. The proposed method is tested on multiple human images and experimentally found appropriate for multiple human detection. The proposed method has been evaluated over two datasets, one is our own created dataset and the other is standard INRIA human detection dataset. Experimental results obtained from the proposed method demonstrate that better discrimination can be achieved for human and non-human objects in real scenes.

关键词： Multiple human detection Multi-resolution analysis Gray scale invariant local binary patterns Moments Support vector machine

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Hidden pattern discovery on epileptic EEG with 1-D local binary patterns and epileptic seizures detection by grey relational analysis

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AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2015年第3期38卷 435-446页

作者： Kaya, Yilmaz Siirt Univ Dept Comp Engn TR-56100 Siirt Turkey

This paper proposes a novel approach to detect epilepsy seizures by using Electroencephalography (EEG), which is one of the most common methods for the diagnosis of epilepsy, based on 1-Dimension local binary Pattern (1D-LBP) and grey relational analysis (GRA) methods. The main aim of this paper is to evaluate and validate a novel approach, which is a computer-based quantitative EEG analyzing method and based on grey systems, aimed to help decision-maker. In this study, 1D-LBP, which utilizes all data points, was employed for extracting features in raw EEG signals, Fisher score (FS) was employed to select the representative features, which can also be determined as hidden patterns. Additionally, GRA is performed to classify EEG signals through these Fisher scored features. The experimental results of the proposed approach, which was employed in a public dataset for validation, showed that it has a high accuracy in identifying epileptic EEG signals. For various combinations of epileptic EEG, such as A-E, B-E, C-E, D-E, and A-D clusters, 100, 96, 100, 99.00 and 100 % were achieved, respectively. Also, this work presents an attempt to develop a new general-purpose hidden pattern determination scheme, which can be utilized for different categories of time-varying signals.

关键词： local binary patterns EEG Hidden patterns Feature extraction Grey relational analysis

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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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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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Moment-based local binary patterns: A novel descriptor for invariant pattern recognition applications

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NEUROCOMPUTING 2013年 99卷 358-371页

作者： Papakostas, G. A. Koulouriotis, D. E. Karakasis, E. G. Tourassis, V. D. TEI Kavala Human Machines Interact HMI Lab Dept Ind Informat Kavala 65404 Greece Democritus Univ Thrace Dept Prod Engn & Management GR-67100 Xanthi Greece

A novel descriptor able to improve the classification capabilities of a typical pattern recognition system is proposed in this paper. The introduced descriptor is derived by incorporating two efficient region descriptors, namely image moments and local binary patterns (LBP), commonly used in pattern recognition applications, in the last decades. The main idea behind this novel feature extraction methodology is the need of improved recognition capabilities, a goal achieved by the combinative use of these descriptors. This collaboration aims to make use of the major advantages each one presents, by simultaneously complementing each other, in order to elevate their weak points. In this way, the useful properties of the moments and moment invariants regarding their robustness to the noise presence, their global information coding mechanism and their invariant behaviour under scaling, translation and rotation conditions, along with the local nature of the LBP, are combined in a single concrete methodology. As a result a novel descriptor invariant to common geometric transformations of the described object, capable to encode its local characteristics, is formed and its classification capabilities are investigated through massive experimental scenarios. The experiments have shown the superiority of the introduced descriptor over the moment invariants, the LBP operator and other well-known from the literature descriptors such as HOG, HOG-LBP and LBP-HF. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Image moments Moment invariants local binary patterns Momentgram Feature extraction Computer-robotic vision Pattern recognition

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Mixed co-occurrence of local binary patterns and Hamming-distance-based local binary patterns

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INFORMATION SCIENCES 2018年 460卷 202-222页

作者： Yuan, Feiniu Xia, Xue Shi, Jinting Shanghai Normal Univ Coll Informat Mech & Elect Engn Shanghai 201418 Peoples R China Jiangxi Univ Finance & Econ Sch Informat Technol Nanchang 330032 Jiangxi Peoples R China Jiangxi Agr Univ Vocat Sch Teachers & Technol Nanchang 330045 Jiangxi Peoples R China

local binary patterns (LBP) have powerful discriminative capabilities. However, traditional methods with LBP histograms cannot capture spatial structures of LBP codes. To extract the spatial structures of an LBP code map, we compute and encode the Hamming distances between LBP codes of a center point and its neighbors on the LBP code map to generate a new code, which is called Hamming-distance-based local binary patterns (HDLBP). Then, we calculate a joint histogram of LBP and HDLBP to represent the LBP co-occurrence with HDLBP (LBPCoHDLBP). Circular bit-wise shift techniques are used to align HDLBP with LBP for rotation invariance. To achieve scale invariance, we extract the feature of LBPCoHDLBP from each scale and concatenate all features of different scales. Finally, we use the sum of absolute differences (SAD) between the intensities of the center point and its neighbors to weight LBPCoHDLBP for further improvement. Extensive experiments show that our method achieves better performance for smoke detection, texture classification and material recognition than most existing methods and is more computationally efficient. (C) 2018 Elsevier Inc. All rights reserved.

关键词： local binary patterns Hamming distance Mixed co-occurrence Smoke detection Image classification

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Detecting seam carving based image resizing using local binary patterns

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COMPUTERS & SECURITY 2015年 55卷 130-141页

作者： Yin, Ting Yang, Gaobo Li, Leida Zhang, Dengyong Sun, Xingming Hunan Univ Sch Informat Sci & Engn Changsha 410082 Hunan Peoples R China China Univ Min & Technol Sch Informat & Elect Engn Xuzhou 221116 Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Jiangsu Peoples R China

Seam carving is the most popular content-aware image retargeting technique. However, it can also be deliberately used for object removal tampering. In this paper, a blind image forensics approach is proposed for seam-carved forgery detection. Since seam carving changes the local texture in an image, a local texture descriptor, i.e., local binary pattern (LBP), is exploited as pre-processing to highlight the local texture artifacts. Moreover, six new half-seam features are defined to unveil the energy changes in half images. They are combined with the existing eighteen energy bias and noise-based features to form twenty-four features. These features are extracted in LBP domain, instead of the conventional pixel-domain to highlight the local texture changes. Finally, support vector machine (SVM) classifier is exploited to determine whether an image is original or suffered from seam carving. Experimental results show that compared with the state-of-the-art methods, the proposed approach improves the detection accuracy by 3.5-19.1% for resized images with different scaling ratios. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Content-aware image retargeting Object removal Seam carving local binary patterns Blind forensic

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