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Improved background modeling of video sequences using spatio-temporal extension of fuzzy local binary pattern

MULTIMEDIA TOOLS AND APPLICATIONS 2019年第12期78卷 17287-17316页

作者： Sefidmazgi, Akram Norouzi Nahvi, Manoochehr Univ Guilan Dept Elect Engn Guilan Iran

Background subtraction is a method of motion detection in video sequences captured by static camera based on construction of a background model and its progressive comparison with each frame of the video. Sometimes the changes in the background objects are not permanent and appear at a rate faster than that of the background update, and this leads to emergence of dynamic textures in the background. As a result, high-quality background modeling plays a major role in motion detection performance, especially in videos with dynamic backgrounds and adverse environmental conditions such as noise. Although local binary pattern (LBP) is a successful methodology for background subtraction, but it cannot properly extract textures from uniform areas of the foreground. In recent years, fuzzy local binary pattern (FLBP) has been developed to improve the performance of LBP operator in texture extraction from images with additive noise. The use of FLBP texture descriptor for background subtraction has led to improved robustness against noise and low sensitivity of the background model to slight changes in the texture gray scale values, and therefore better texture extraction from uniform areas, even in the presence of dynamic backgrounds and adverse environmental conditions. But despite this improvement, this operator is still sensitive to time-variations of pixels in dynamic backgrounds. To avoid this issue and incorporate correlation of pixel values over successive frames, this study proposes the use of spatio-temporal extension of FLBP with symmetry about central pixel in combination with the local Color Histogram (LCH) in the Improved Hue Luminance and Saturation (IHLS) color space for describing the pixel color features. The results of tests conducted with standard databases show that for dynamic backgrounds, the use of proposed Spatio-Temporal fuzzy Center Symmetric local binary pattern (STFCS-LBP) operator with the spatio-temporal neighborhood texture patterns and the local

关键词： Background subtraction Video analysis Background modeling fuzzy local binary pattern Dynamic texture IHLS color space

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Texture Feature Extraction Using Intuitionistic fuzzy local binary pattern

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JOURNAL OF INTELLIGENT SYSTEMS 2020年第1期29卷 19-34页

作者： Ansari, Mohd Dilshad Ghrera, Satya Prakash Mishra, Arunodaya Raj Jaypee Univ Informat Technol Dept Comp Sci & Engn Solan Himachal Prades India ITM Univ Dept Math Gwalior Madhya Pradesh India

In this paper, intuitionistic fuzzy local binary for texture feature extraction (IFLBP) has been proposed to encode local texture from the input image. The proposed method extends the fuzzy local binary pattern approach by incorporating intuitionistic fuzzy sets in the representation of local patterns of texture in images. Intuitionistic fuzzy local binary pattern also contributes to more than one bin in the distribution of IFLBP values, which can further be used as a feature vector in the various fields of image processing. The performance of the proposed method has been demonstrated on various medical images and processing images of size 256 x 256. The obtained results validated the effectiveness and usefulness of our proposed method over the other reported methods, and new improvements are suggested.

关键词： fuzzy local binary pattern intuitionistic fuzzy sets intuitionistic fuzzy local binary pattern entropy

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Identification of Medicinal Plant by fuzzy local binary pattern and Multi Objective Genetic Algorithm 5

Identification of Medicinal Plant by Fuzzy Local Binary Patt...

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International Conference on Computer, Control, Informatics and its Applications (IC3INA)

作者： Laxmi, Gibtha Fitri Herdiyeni, Yeni Arkeman, Yandra Ibn Khaldun Univ KH Sholeh Iskandar St Km2 Bogor Indonesia Bogor Agr Univ Bogor Indonesia

ISBN: (纸本)9781538639788

This research proposes multi-objective genetic algorithm non-dominated-sorting (MOGA NSGA-II) of fuzzy local binary pattern to optimize LBP operator and fuzzy threshold for identification of Indonesian medicinal plants. Multi-objective genetic algorithm (MOGA) is the genetic algorithm (GA) which is developed specifically for problems with multiple objectives. We evaluated 1,440 medicinal plant leaf images which belong to 30 species. The images were taken from Biofarmaka IPB, Cikabayan Farm, Greenhouse Center Ex-Situ Conservation of Medicinal Plant Indonesia Tropical Forest and Gunung Leutik. FLBP is used to handle uncertainty on images with various patterns. FLBP approach is based on the assumption that a local image neighbourhood may be characterized by more than a single binary pattern. The experimental results show that the correct selection of FLBP operator and threshold using MOGA can reach accuracy of 85%. It can be concluded that this propose method is capable to identify medicinal plants species efficiently and accurately.

关键词： fuzzy local binary pattern Multi-Objective Genetic Algorithm Texture Threshold

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Mobile Application for Indonesian Medicinal Plants Identification using fuzzy local binary pattern and fuzzy Color Histogram

Mobile Application for Indonesian Medicinal Plants Identific...

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International Conference on Advanced Computer Science and Information Systems (ICACSIS)

作者： Herdiyeni, Yeni Wahyuni, Ni Kadek Sri Bogor Agr Univ Fac Math & Nat Sci Bogor Indonesia

ISBN: (纸本)9781467330268;9789791421157

This research proposed a new mobile application based on Android operating system for identifying Indonesian medicinal plant images based on texture and color features of digital leaf images. In the experiments we used 51 species of Indonesian medicinal plants and each species consists of 48 images, so the total images used in this research are 2,448 images. This research investigates effectiveness of the fusion between the fuzzy local binary pattern (FLBP) and the fuzzy Color Histogram (FCH) in order to identify medicinal plants. The FLBP method is used for extracting leaf image texture. The FCH method is used for extracting leaf image color. The fusion of FLBP and FCH is done by using Product Decision Rules (PDR) method. This research used Probabilistic Neural Network (PNN) classifier for classifying medicinal plant species. The experimental results show that the fusion between FLBP and FCH can improve the average accuracy of medicinal plants identification. The accuracy of identification using fusion of FLBP and FCH is 74.51%. This application is very important to help people identifying and finding information about Indonesian medicinal plant.

关键词： fuzzy local binary pattern fuzzy color histogram Indonesian medicinal plant identification probabilistic neural network

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Adaptive fuzzy local binary pattern for Texture Classification 2

Adaptive Fuzzy Local Binary Pattern for Texture Classificati...

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2nd International Conference on Man and Machine Interfacing (MAMI)

作者： Girish, Gandham Dash, Jatindra Kumar Natl Inst Sci & Technol Dept Elect & Commun Engn Berhampur 761008 Odisha India Natl Inst Sci & Technol Dept Comp Sci & Engn Berhampur 761008 Odisha India

ISBN: (纸本)9781538629895

Many local texture features are proposed for texture classification and retrieval. fuzzy local binary pattern (FLBP) is one of such promising texture descriptor found in the literature. However, the fuzzification parameter (T) used in FLBP is computed empirically and not adaptive to local content. In addition, one need to perform several experiments over a large range of T to find its optimal value. In this paper a novel technique is proposed to compute fuzzification parameter and the resulting descriptor is named as Adaptive fuzzy local binary pattern (AFLBP). In the proposed technique, the computation of fuzzyfication parameter is adaptive in nature and thereby capture most representative feature for each pixel in the image. The earlier paper makes use of Support Vector Machine (that uses a non-linear kernel) to test the discriminating ability of the FLBP descriptor. Here, in this paper, the K-Nearest Neighbors (kNN) classifier is used as a classifier to investigate the discriminating strength of the proposed feature descriptor. The discriminating ability of the proposed texture descriptor is compared with that of local binary pattern (LBP) and fuzzy local binary pattern (FLBP) using Brodatz texture database. The result shows that the proposed descriptor outperforms other competing descriptors used in the experiment irrespective of the distance measure used for classification. It is also observed that the k-NN classifier using Manhattan distance outperforms all other combinations.

关键词： local binary pattern fuzzy local binary pattern Texture Classification

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Quantized fuzzy LBP for face recognition 40

Quantized fuzzy LBP for face recognition

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Ren, Jianfeng Jiang, Xudong Yuan, Junsong BeingThere Centre Institute of Media Innovation Nanyang Technological University 50 Nanyang Drive Singapore Singapore School of Electrical and Electronics Engineering Nanyang Technological University 50 Nanyang Avenue Singapore Singapore

ISBN: (纸本)9781467369978

Face recognition under large illumination variations is challenging. local binary pattern (LBP) is robust to illumination variation, but sensitive to noise. fuzzy LBP (FLBP) partially solves the noise-sensitivity problem by incorporating fuzzy logic in the representation of local binary patterns. The fuzzy membership function is determined by both sign and magnitude of the pixel difference. However, the magnitude is easily altered by noise, hence could be unreliable. Thus, we propose to determine the fuzzy membership function by its sign only. We name the proposed approach as Quantized fuzzy LBP (QFLBP). On two challenging face recognition datasets, it is shown more robust to noise, and demonstrates a superior performance to FLBP and many other LBP variants. © 2015 IEEE.

关键词： Face Recognition fuzzy local binary pattern Quantized fuzzy LBP

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Quantized fuzzy LBP for face recognition

Quantized fuzzy LBP for face recognition

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： J. Ren X. Jiang J. Yuan Inst. of Media Innovation Nanyang Technol. Univ. Singapore Singapore

ISBN: (纸本)9781467369985

关键词： Face Recognition fuzzy local binary pattern Quantized fuzzy LBP Facial Recognition fuzzy membership function LBP gene LBP fuzzy logic fuzzy Noise

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