检索结果-内蒙古大学图书馆

24th Signal Processing and Communication Application Conference (SIU)

作者： Kazak, Nihan Koc, Mehmet Benligiray, Burak Topal, Cihan Bilecik Seyh Edebali Univ Bilgisayar Muh Bol Bilecik Turkey Bilecik Seyh Edebali Univ Elek Elekt Muh Bol Bilecik Turkey Anadolu Univ Elek Elekt Muh Bol Eskisehir Turkey

ISBN: (纸本)9781509016792

Texture recognition is an important tool used for content-based image retrieval, face recognition, and satellite image classification applications. One of the most successful features for texture recognition is local binary patterns (LBP), which computes local intensity differences for a pixel with respect to its neighbor pixels. In many studies in the literature, histogram based similarity measures are employed to classify LBP features. In this study, we investigate the performance of support vector machines, linear discriminant analysis, and linear regression classifier to improve the success of LBP features. We achieved 84.4% classification success using linear regression classification.

关键词： texture classification local binary patterns classification methods UIUC texture database

来源：评论

学校读者我要写书评

暂无评论

Improving the Face Recognition Accuracy under Varying Illumination Conditions for local binary patterns and local Ternary patterns based on Weber-Face and Singular Value Decomposition 3

Improving the Face Recognition Accuracy under Varying Illumi...

引用

3rd International Conference on Green Technology and Sustainable Development (GTSD)

作者： Chi-Kien Tran Tseng, Chin-Dar Lee, Tsair-Fwu Natl Kaohsiung Univ Appl Sci Dept Elect Engn Kaohsiung Taiwan Hanoi Univ Ind Hanoi Vietnam Kaohsiung Med Univ Grad Inst Clin Med Kaohsiung 807 Taiwan

ISBN: (纸本)9781509036387

This paper addresses a new approach based on the Weber-face and singular value decomposition (SVD) methods to improve the recognition accuracy for a face recognition system using local binary patterns and local ternary patterns in an illumination variation environment. The face images are the first extracted illumination-invariant components by the Weber-face method. Secondly, SVD is applied to the encoded images. Next, the training encoded images are extracted features based on local binary patterns or local ternary patterns. Finally, in the classification phase, the singular value matrix of a test image is combined with those of the training images to adjust the illumination of the test image before the features are extracted and classified. The recognition is performed using a nearest neighbor classifier with Chi-square as a dissimilarity measure. Experimental results on the extended Yale B database demonstrated the efficiency of our proposed method. Thus, the proposed approach is expected to contribute to the face recognition problem under varying illumination conditions.

关键词： face recognition illumination pretreatment Weber-face singular value decomposition local binary patterns local ternary patterns

来源：评论

学校读者我要写书评

暂无评论

THREE-DIMENSIONAL local binary patterns FOR HYPERSPECTRAL IMAGERY CLASSIFICATION 36

THREE-DIMENSIONAL LOCAL BINARY PATTERNS FOR HYPERSPECTRAL IM...

引用

36th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Jia, Sen Hu, Jie Deng, Lin Jia, Xiuping Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Univ New South Wales Sch Engn & Informat Technol Canberra ACT Australia

ISBN: (纸本)9781509033324

local binary patterns (LBP) features extracted from hyperspectral imagery (HSI) have gained impressive performance in hyperspectral classification tasks, for which LBP got considerable attention. However, existing LBP-based hyperspectral imagery classification methods utilized two-dimensional LBP (2DLBP) that could capture gray variation signal in space, which did not excavate the contextual information that hides in spectral-spatial structure considering that hyperspectral imagery characterizes by three dimension. Aimed at this problem, this paper presents a three-dimensional LBP-based (3DLBP) hyperspectral imagery classification method where 2DLBP textures histogram on three orthogonal planes are concatenated to form 3DLBP texture features to be classified by sparse representation. A serial of experiments are conducted on the Pavia university dataset, and the experimental results show that the performance of 3DLBP is significantly superior to that of 2DLBP.

关键词： HSI classification local binary patterns SRC

来源：评论

学校读者我要写书评

暂无评论

Laughter Detection based on the Fusion of local binary patterns, Spectral and Prosodic Features 18

Laughter Detection based on the Fusion of Local Binary Patte...

引用

18th IEEE International Workshop on Multimedia Signal Processing (MMSP)

作者： Bedoya, Stefany Falk, Tiago H. Univ Quebec INRS EMT Montreal PQ Canada

ISBN: (纸本)9781509037247

Today, great focus has been placed on context-aware human-machine interaction, where systems are aware not only of the surrounding environment, but also about the mental/affective state of the user. Such knowledge can allow for the interaction to become more human-like. To this end, automatic discrimination between laughter and speech has emerged as an interesting, yet challenging problem. Typically, audio- or video-based methods have been proposed in the literature;humans, however, are known to integrate both sensory modalities during conversation and/or interaction. As such, this paper explores the fusion of support vector machine classifiers trained on local binary pattern (LBP) video features, as well as speech spectral and prosodic features as a way of improving laughter detection performance. Experimental results on the publicly-available MAHNOB Laughter database show that the proposed audio-visual fusion scheme can achieve a laughter detection accuracy of 93.3%, thus outperforming systems trained on audio or visual features alone.

关键词： speech laughter spectral features cepstral features local binary patterns

来源：评论

学校读者我要写书评

暂无评论

Sub Oriented Histograms of local binary patterns for Smoke Detection and Texture Classification

引用

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2016年第4期10卷 1807-1823页

作者： Yuan, Feiniu Shi, Jinting Xia, Xue Yang, Yong Fang, Yuming Wang, Rui Jiangxi Univ Finance & Econ Sch Informat Technol Nanchang 330032 Jiangxi Peoples R China Jiangxi Agr Univ Vocat Sch Teachers & Technol Nanchang 330032 Jiangxi Peoples R China Jiangxi Univ Finance & Econ Sch Software & Commun Engn Nanchang 330032 Jiangxi Peoples R China

local binary Pattern (LBP) and its variants have powerful discriminative capabilities but most of them just consider each LBP code independently. In this paper, we propose sub oriented histograms of LBP for smoke detection and image classification. We first extract LBP codes from an image, compute the gradient of LBP codes, and then calculate sub oriented histograms to capture spatial relations of LBP codes. Since an LBP code is just a label without any numerical meaning, we use Hamming distance to estimate the gradient of LBP codes instead of Euclidean distance. We propose to use two coordinates systems to compute two orientations, which are quantized into discrete bins. For each pair of the two discrete orientations, we generate a sub LBP code map from the original LBP code map, and compute sub oriented histograms for all sub LBP code maps. Finally, all the sub oriented histograms are concatenated together to form a robust feature vector, which is input into SVM for training and classifying. Experiments show that our approach not only has better performance than existing methods in smoke detection, but also has good performance in texture classification.

关键词： Sub Oriented Histogram Smoke detection Image Classification local binary patterns Support Vector Machine

来源：评论

学校读者我要写书评

暂无评论

Automatic identification of butterfly species based on local binary patterns and artificial neural network

引用

APPLIED SOFT COMPUTING 2015年 28卷 132-137页

作者： Kaya, Yilmaz Kayci, Lokman Uyar, Murat Siirt Univ Dept Comp Engn TR-56100 Siirt Turkey Siirt Univ Dept Biol TR-56100 Siirt Turkey Siirt Univ Dept Elect & Elect Engn TR-56100 Siirt Turkey

Butterflies are classified firstly according to their outer morphological qualities. It is required to analyze genital characters of them when classification according to outer morphological qualities is not possible. Genital characteristics of a butterfly can be determined by using various chemical substances and methods. Currently, these processes are carried out manually by preparing genital slides of the collected butterfly through some certain processes. For some groups of butterflies molecular techniques should be applied for identification which is expensive to use. In this study, a computer vision method is proposed for automatically identifying butterfly species as an alternative to conventional identification methods. The method is based on local binary pattern (LBP) and artificial neural network (ANN). A total of 50 butterfly images of five species were used for evaluating the effectiveness of the proposed method. Experimental results demonstrated that the proposed method has achieved well recognition in terms of accuracy rates for butterfly species identification. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Butterfly identification local binary patterns Texture features Artificial neural network

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Detecting seam carving based image resizing using local binary patterns

引用

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

来源：评论

学校读者我要写书评

暂无评论

Efficient levels of spatial pyramid representation for local binary patterns

引用

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：