检索结果-内蒙古大学图书馆

Efficient levels of spatial pyramid representation for local binary patterns

IET COMPUTER VISION 2015年第6期9卷 871-883页

作者： Akoushideh, Alireza Maybodi, Babak Mazloom-Nezhad Shahid Beheshti Univ Elect & Comp Dept Tehran Iran

local binary patterns (LBPs) are a well-known operator that shows the ability for rotation and scale invariant texture classification. A recent extension of this operator is the pyramid transform domain approach on LBPs (PLBP). Obtaining more accuracy by using more pyramid representations is an important result of PLBP, which increases not only feature dimensionality, but also classification computational time (CT). This study illustrates that more pyramid image representations will not improve the performance of the PLBP. We evaluate efficient levels of representation for the PLBP descriptor. In addition, the authors propose some feature selection approaches, such as the multi-level and multi-resolution (ML + MR) approach and the ML, MR and multi-band (ML + MR + MB) approach and discuss their efficiency and CT. Experimental results show that the proposed feature selection approaches improve the accuracy of texture classification with fewer pyramid image representations. In addition, replacing the Chi-2 similarity measurement with Czekannowski improves the accuracy of texture classification.

关键词： image texture image classification image representation spatial pyramid representation local binary patterns scale invariant texture classification rotation invariant texture classification pyramid transform domain approach classification computational time pyramid image representations feature selection approaches multiresolution approach texture classification Czekannowski

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Automatic detection and segmentation of bovine corpora lutea in ultrasonographic ovarian images using genetic programming and rotation invariant local binary patterns

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2013年第4期51卷 405-416页

作者： Dong, Meng Eramian, Mark G. Ludwig, Simone A. Pierson, Roger A. Univ Saskatchewan Dept Comp Sci Saskatoon SK S7N 0W0 Canada N Dakota State Univ Dept Comp Sci Fargo ND 58105 USA Univ Saskatchewan Dept Obstet Gynecol & Reprod Sci Saskatoon SK S7N 0W0 Canada

In this study, we propose a fully automatic algorithm to detect and segment corpora lutea (CL) using genetic programming and rotationally invariant local binary patterns. Detection and segmentation experiments were conducted and evaluated on 30 images containing a CL and 30 images with no CL. The detection algorithm correctly determined the presence or absence of a CL in 93.33 % of the images. The segmentation algorithm achieved a mean (+/- standard deviation) sensitivity and specificity of 0.8693 +/- A 0.1371 and 0.9136 +/- A 0.0503, respectively, over the 30 CL images. The mean root mean squared distance of the segmented boundary from the true boundary was 1.12 +/- A 0.463 mm and the mean maximum deviation (Hausdorff distance) was 3.39 +/- A 2.00 mm. The success of these algorithms demonstrates that similar algorithms designed for the analysis of in vivo human ovaries are likely viable.

关键词： Texture analysis Ultrasonography Corpora lutea local binary patterns Genetic programming

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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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Automated gait-based gender identification using fuzzy local binary patterns with tuned parameters

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2019年第7期10卷 2495-2504页

作者： El-Alfy, E-S M. Binsaadoon, A. G. King Fand Univ Petr & Minerals Coll Comp Sci & Engn Informat & Comp Sci Dept Dhahran 31261 Saudi Arabia

With the rapid advances in sensor networks and internet-of-things, several applications have evolved to ubiquitously monitor what is happening in smart environments. This paper designs a gait-based gender identification system based on an improved method of fuzzy local binary patterns for texture analysis. The moving person's silhouette is extracted and a texture image is constructed to summarize structural and dynamical variations over one gait cycle. Then, histograms representing soft pattern variations are constructed and used as feature vectors. The proposed method employs grid search to find the optimal hyper-parameters for the feature extraction method. Finally, support vector machines are trained to predict the gender of the walking person. The performance is evaluated and compared with four other local binary pattern (LBP) related texture descriptors. The results show significant improvements in the performance under different walking conditions.

关键词： Surveillance video Gender recognition Gait energy image Support vector machine Texture feature extraction local binary patterns

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Face verification using local binary patterns and generic model adaptation

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INTERNATIONAL JOURNAL OF BIOMETRICS 2015年第1期7卷 31-44页

作者： Boutellaa, Elhocine Harizi, Farid Bengherabi, Messaoud Ait-Aoudia, Samy Hadid, Abdenour Ctr Dev Technol Avancees BP 17 Baba Haasen 16081 Algeria Ecole Natl Super Informat Oued Smar 16270 Algeria Univ Oulu FI-90014 Oulu Finland

The popular local binary patterns (LBP) have been highly successful in representing and recognising faces. However, the original LBP-based face recognition method has some problems that need to be addressed. In this work, we propose two approaches to address the histogram representation drawbacks in the LBP-based face verification system. The first approach employs vector quantization maximum a posteriori adaptation (VQMAP) model, where a generic face model is obtained by vector quantisation and the user models are inferred using maximum a posteriori adaptation. The second approach proposes an enhanced LBP histogram representation by adapting a generic face histogram to each user. Moreover, the two proposed approaches are further fused to enhance the verification performance. We evaluate our proposed approaches on two publicly available databases, namely BANCA and XM2VTS, and compare the results against the original LBP approach and its variants, demonstrating very promising results.

关键词： local binary patterns LBPs vector quantisation maximum a posteriori adaptation face verification histogram adaptation

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

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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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Computing the Principal local binary patterns for face recognition using data mining tools

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第8期39卷 7165-7172页

作者： Pujol, Francisco A. Carlos Garcia, Juan Univ Alicante Dpto Tecnol Informat & Computac E-03080 Alicante Spain

local binary patterns are considered as one of the texture descriptors with better results;they employ a statistical feature extraction by means of the binarization of the neighborhood of every image pixel with a local threshold determined by the central pixel. The idea of using local binary patterns for face description is motivated by the fact that faces can be seen as a composition of micro-patterns which are properly described by this operator and, consequently, it has become a very popular technique in recent years. In this work, we show a method to calculate the most important or Principal local binary patterns for recognizing faces. To do this, the attribute evaluator algorithm of the data mining tool Weka is used. Furthermore, since we assume that each face region has a different influence on the recognition process, we have designed a 9-region mask and obtained a set of optimized weights for this mask by means of the data mining tool RapidMiner. Our proposal was tested with the FERET database and obtained a recognition rate varying between 90% and 94% when using only 9 uniform Principal local binary patterns, for a database of 843 individuals;thus, we have reduced both the dimension of the feature vectors needed for completing the recognition tasks and the processing time required to compare all the faces in the database. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Data mining local binary patterns Face recognition

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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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Gender and texture classification: A comparative analysis using 13 variants of local binary patterns

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PATTERN RECOGNITION LETTERS 2015年第Part2期68卷 231-238页

作者： Hadid, Abdenour Ylioinas, Juha Bengherabi, Messaoud Ghahramani, Mohammad Taleb-Ahmed, Abdelmalik Univ Oulu Ctr Machine Vis Res SF-90100 Oulu Finland CDTA Algiers Algeria Univ Valenciennes & Hainaut Cambresis Valence France

Among very popular local image descriptors which has shown interesting results in extracting soft facial biometric traits is the local binary patterns (LBP). LBP is a gray scale invariant texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel with the value of the center pixel and considers the result as a binary number. LBP labels can be regarded as local primitives such as curved edges, spots, flat areas etc. These labels or their statistics, most commonly the histogram, can then be used for further image analysis. Due to its discriminative power and computational simplicity, the LBP methodology has already attained an established position in computer vision. LBP is also very flexible: it can be easily adapted to different types of problems and used together with other image descriptors. Since its introduction. LBP has inspired plenty of new methods, thus revealing that texture based region descriptors can be very efficient in representing different images. Nowadays, many LBP variants can be found in the literature. This article reviews 13 variants and provides a comparative analysis on two different problems (gender and texture classification) using benchmark databases. The experiments show that basic LBP provides good results and generalizes well to different problems and hence can be a good starting point when trying to find an optimal variant for a given application. The best results are obtained with BSIF (binarized statistical image features) but at the cost of higher computational time compared to basic LOP. Furthermore, experiments on combining three best performing descriptors are conducted, pointing out useful insight into their complementarity. (C) 2015 Elsevier B.V. All rights reserved.

关键词： local binary patterns Texture analysis Gender classification Face analysis

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Wavelet local binary patterns fusion as illuminated facial image preprocessing for face verification

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第4期38卷 3959-3972页

作者： Goh, Yi Zheng Teoh, Andrew Beng Jin Goh, Michael Kah Ong Yonsei Univ Coll Engn Sch Elect & Elect Engn Seoul 120749 South Korea Multimedia Univ FIST Melaka 75450 Malaysia

Poor illumination condition is recognized as one of the major problem in contemporary two-dimensional (2D) face verification system. It causes large variation in facial images and degrades the performance of the system. Many works of resolving illumination variation in face verification have been reported in the past decades. In this paper, a facial image illumination invariant technique is devised based on the fusion of wavelet analysis and local binary patterns. Particularly, illumination-reflectance model is used to detach illumination and reflectance components with multi-resolution nature of wavelet analysis. The illumination component that resides in low spatial-frequency wavelet subband is first rid off efficiently. The reflectance components that reside in high and middle spatial-frequency wavelet subbands are enhanced with local binary patterns histogram. Finally, two processed images are fused through wavelet image fusion. This technique works out promisingly in achieving better recognition results on YaleB, CMU PIE and FRGC face databases in comparison with existing illumination invariant techniques. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Illumination-reflectance model Wavelet local binary patterns Wavelet fusion Histogram specification Preprocessing

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