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A local binary pattern based texture descriptors for classification of tea leaves

NEUROCOMPUTING 2015年 168卷 1011-1023页

作者： Tang, Zhe Su, Yuancheng Er, Meng Joo Qi, Fang Zhang, Li Zhou, Jianyong Cent S Univ Sch Informat Sci & Engn Changsha 410083 Peoples R China Nanyang Technol Univ Coll Engn Sch Elect & Elect Engn Singapore 639798 Singapore Changsha Xiangfeng Tea Machinery Mfg Co Ltd Changsha 410100 Hunan Peoples R China

For tea processing production lines, different fresh tea leaves require different processing parameters for the control systems of tea machines. Hence, an effective algorithm for classification of tea leaves will be important for automatic tea processing. However, most of tea classification researches were focused on gross tea, instead of fresh tea leaves. In this paper, a texture extraction method combing a non-overlap window local binary pattern (LBP) and Gray Level Co-Occurrence Matrix (GLCM) has been proposed for green tea leaves classification. By taking advantages of both LBP and GLCM for texture extraction, this method is able to effectively extract texture of tea leaves for classification at low computational cost to meet automatic tea production line requirements. The experiments have been conducted to prove the effectiveness of the proposed method. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Tea classification local binary pattern Gray level co-occurrence matrix Texture classification

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Adjacent evaluation of local binary pattern for texture classification

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2015年 33卷 323-339页

作者： Song, Kechen Yan, Yunhui Zhao, Yongjie Liu, Changsheng Northeastern Univ Sch Mech Engn & Automat Shenyang Peoples R China Northeastern Univ Key Lab Anisotropy & Texture Mat Minist Educ Shenyang Peoples R China

This paper presents a novel, simple, yet robust texture descriptor against noise named the adjacent evaluation local binary patterns (AELBP) for texture classification. In the proposed approach, an adjacent evaluation window is constructed to modify the threshold scheme of LBP. The neighbors of the neighborhood center g(c) are set as the evaluation center a(p). Surrounding the evaluation center, we set up an evaluation window and calculate the value of a(p), and then extract the local binary codes by comparing the value of a(p) with the value of the neighborhood center g(c). Moreover, this adjacent evaluation method is generalized and can be integrated with the existing LBP variants such as completed local binary pattern (CLBP) and local ternary pattern (LTP) to derive new image features against noise for texture classification. The proposed approaches are compared with the state-of-the-art approaches on Outex and CUReT databases, and evaluated on three challenging databases (i.e. UIUC, UMD and ALOT databases) for texture classification. Experimental results demonstrate that the proposed approaches present a solid power of texture classification under illumination and rotation variations, significant viewpoint changes, and significant large-scale challenging conditions. Furthermore, the proposed approaches are more robust against noise and consistently outperform all the basic approaches in comparison. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Adjacent evaluation local binary pattern Completed local binary pattern local ternary pattern Rotation invariance Texture classification Texture descriptor Texture database

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Sorted Consecutive local binary pattern for Texture Classification

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2015年第7期24卷 2254-2265页

作者： Ryu, Jongbin Hong, Sungeun Yang, Hyun S. Korea Adv Inst Sci & Technol Dept Comp Sci Taejon 305701 South Korea

In this paper, we propose a sorted consecutive local binary pattern (scLBP) for texture classification. Conventional methods encode only patterns whose spatial transitions are not more than two, whereas scLBP encodes patterns regardless of their spatial transition. Conventional methods do not encode patterns on account of rotation-invariant encoding;on the other hand, patterns with more than two spatial transitions have discriminative power. The proposed scLBP encodes all patterns with any number of spatial transitions while maintaining their rotation-invariant nature by sorting the consecutive patterns. In addition, we introduce dictionary learning of scLBP based on kd-tree which separates data with a space partitioning strategy. Since the elements of sorted consecutive patterns lie in different space, it can be generated to a discriminative code with kd-tree. Finally, we present a framework in which scLBPs and the kd-tree can be combined and utilized. The results of experimental evaluation on five texture data sets-Outex, CUReT, UIUC, UMD, and KTH-TIPS2-a-indicate that our proposed framework achieves the best classification rate on the CUReT, UMD, and KTH-TIPS2-a data sets compared with conventional methods. The results additionally indicate that only a marginal difference exists between the best classification rate of conventional methods and that of the proposed framework on the UIUC and Outex data sets.

关键词： local binary pattern texton dictionary texture classification

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Perceptual Image Hashing Using Block Truncation Coding and local binary pattern

Perceptual Image Hashing Using Block Truncation Coding and L...

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Asia-Pacific-Signal-and-Information-Processing-Association Annual Summit and Conference (APSIPA ASC)

作者： Chen, Xueqin Qin, Chuan Ji, Ping Univ Shanghai Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9789881476807

In this paper, we propose a novel image hashing scheme based on block truncation coding (BTC) and local binary pattern (LBP), which can be applied in image authentication and retrieval. In the proposed scheme, the pre-processing is first conducted on input image by bilinear interpolation, Gaussian low pass filtering, and singular value decomposition (SVD) to construct a secondary image for regularization. Then, BTC is applied on the secondary image to obtain the high/low quantized levels and the corresponding binary map that can reflect the contents of image. The concatenated image feature sequence is generated with the assist of the center-symmetrical local binary pattern (CSLBP). Finally, data dimensionality reduction is exploited on the image feature sequence to produce the part of hash. Combined with high/low quantized-levels, the final hash can be obtained. Experimental results show that the proposed scheme has the satisfactory performances of robustness, anti-collision, and security.

关键词： local binary pattern

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Novel Face Recognition Algorithm based on Adaptive 3D local binary pattern Features and Improved Singular Value Decomposition Method

Novel Face Recognition Algorithm based on Adaptive 3D Local ...

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IEEE International Conference on Inventive Computation Technologies (ICICT)

作者： Li, Yang Xidian Univ Sch Elect Engn Xian Shaanxi Peoples R China

Face recognition is a kind of important method focused on biological information identification, which is also a research hotspot in the field of pattern recognition and machine vision. In recent years, some pattern recognition researches show that, human visual system uses a lot of visual-based deep information. Therefore, for face recognition in complex environment, we have research focus on depth images based face recognition system, in order to overcome the problem that the 2-D face recognition system is so sensitive to pose, facial expression and illumination changes. It is remarkable that when we apply statistical method to solve the problems of face depth images recognition, we extremely design feature extraction algorithm for specific training sample set. Nevertheless, once these feature extraction algorithms is completed, there will never be any improvement among them. Thus, this situation leads to the poor universality of the feature extraction algorithms, and the effectiveness and stability of the algorithm will be significantly decreased. As the result, the performance of the recognition system is finally affected. In this paper, we focus on the universality problem of feature extraction algorithm and system identification performance, combining feedback learning theory with Neural Network theory and 3-D local binary pattern feature extraction process. We propose a novel face recognition algorithm based on adaptive 3-D local binary pattern and Singular Value Decomposition method. In the process of face recognition, the most important part is facial feature extraction, by the way, Singular Value Decomposition method regards the face images as a matrix, and obtain image features by segmenting face images. The experimental simulation results show that our algorithm has good feature extraction effect and face recognition performance. We also compare our algorithm with other state-of-the-art methodologies and obtain the better effectiveness.

关键词： Face recognition local binary pattern Singular Value Decomposition Feature extraction

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Facial Expression Recognition Using Modified local binary pattern 1

Facial Expression Recognition Using Modified Local Binary Pa...

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1st International Conference on Computational Intelligence in Data Mining (ICCIDM)

作者： Biswas, Suparna Sil, Jaya Gurunanak Inst Technol Dept Elect & Commun Engn Kolkata India Indian Inst Engn Sci & Technol Univ Dept Comp Sci & Technol Sibpur Howrah India

ISBN: (纸本)9788132222088;9788132222071

In this paper a low computation feature space has been proposed to recognize expressions of face images. The image is divided into number of blocks and binary pattern corresponding to each block is generated by modifying the local binary pattern (LBP). The proposed method generates compressed binary pattern of images and therefore, reduced in size. Features are extracted from transformed image using block wise histograms with variable number of bins. For classification we use two techniques, template matching and Support Vector Machine (SVM). Experiments on face images with different resolutions show that the proposed approach performs well for low resolution images. Considering Cohn-Kanade database, the proposed method is compared with LBP feature based methods demonstrating better performance.

关键词： Facial expression Template matching local binary pattern

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Cancellability for local binary pattern Biometric Authentication

Cancellability for Local Binary Pattern Biometric Authentica...

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Asia-Pacific-Signal-and-Information-Processing-Association Annual Summit and Conference (APSIPA ASC)

作者： Syarif, Munalih Ahmad Tiong, Leslie Ching Ow Goh, Alwyn Nen, Latifah Mat Lee, Kay Win MIMOS Berhad Technol Pk Malaysia Kuala Lumpur 57000 Malaysia

ISBN: (纸本)9789881476807

Biometric cancellability enables protection and revocation of sensitive biometric data as used for authentication. This paper describes a cancellable biometric implementation by means of randomised local binary pattern (LBP) feature vector, with user-specific secret-keys used to generate a biometric template. We also present experimental results to establish that authentication undertaken by our methodology is reliable, and also that the objectives of revocability and diversity are accomplished.

关键词： local binary pattern

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MEDIAN ROBUST EXTENDED local binary pattern FOR TEXTURE CLASSIFICATION

MEDIAN ROBUST EXTENDED LOCAL BINARY PATTERN FOR TEXTURE CLAS...

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IEEE International Conference on Image Processing (ICIP)

作者： Liu, Li Fieguth, Paul Pietikainen, Matti Lao, Songyang Natl Univ Def Technol Sch Informat Syst & Management Changsha 410073 Hunan Peoples R China Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada Univ Oulu Dept Comp Sci & Engn Ctr Machine Vis Res Oulu 90014 Finland

ISBN: (纸本)9781479983391

local binary patterns (LBP) are among the most computationally efficient amongst high-performance texture features. However, LBP is very sensitive to image noise and is unable to capture macrostructure information. To best address these disadvantages, in this paper we introduce a novel descriptor for texture classification, the Median Robust Extended local binary pattern (MRELBP). In contrast to traditional LBP and many LBP variants, MRELBP compares local image medians instead of raw image intensities. We develop a multiscale LBP-type descriptor by efficiently comparing image medians over a novel sampling scheme, which can capture both microstructure and macrostructure. A comprehensive evaluation on benchmark datasets reveals MRELBP's remarkable performance (robust to gray scale variations, rotation changes and noise) relative to state-of-the-art algorithms, but nevertheless at a low computational cost, producing the best classification scores of 99.82%, 99.38% and 99.77% on three popular Outex test suites. Furthermore, MRELBP is also shown to be highly robust to image noise including Gaussian noise, Gaussian blur, Salt-and-Pepper noise and random pixel corruption.

关键词： Texture classification Feature extraction local binary pattern local descriptors Median filtering

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Discriminative local binary pattern for Image Feature Extraction 16th

Discriminative Local Binary Pattern for Image Feature Extrac...

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16th International Conference on Computer Analysis of Images and patterns (CAIP)

作者： Kobayashi, Takumi Natl Inst Adv Ind Sci & Technol Tsukuba Ibaraki Japan

ISBN: (纸本)9783319231921;9783319231914

local binary pattern (LBP) is widely used to extract image features in various visual recognition tasks. LBP is formulated in quite a simple form and thus enables us to extract effective image features with a low computational cost. There, however, are some limitations mainly regarding sensitivity to noise and loss of image contrast information. In this paper, we propose a novel LBP-based image feature to remedy those drawbacks without degrading the simplicity of the original LBP formulation. Encoding local pixel intensities into binary patterns can be regarded as separating them into two modes (clusters). We introduce Fisher discriminant criterion to optimize the LBP coding for exploiting binary patterns stably and discriminatively with robustness to noise. Besides, image contrast information is incorporated in a unified way by leveraging the discriminant score as a weight on the corresponding binary pattern;thereby, the prominent patterns are emphasized. In the experiments on pedestrian detection, the proposed method exhibits superior performance compared to the ordinary LBP and the other methods, especially in the case of lower-dimensional features.

关键词： Visual recognition Image feature local binary pattern Discriminant criterion

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Face Recognition using Augmented local binary pattern and Bray Curtis Dissimilarity Metric 2

Face Recognition using Augmented Local Binary Pattern and Br...

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2nd International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Shyam, Radhey Singh, Yogendra Narain Inst Engn & Technol Deptartment Comp Sci & Engn Lucknow 226021 Uttar Pradesh India

ISBN: (纸本)9781479959914

The panorama of face recognition is to find the dissimilarity features that can be used to discriminate individuals for their recognition. The holistic face recognition methods available in the literature perform well in the controlled environments. These methods may not appropriate to identify people through their faces in uncontrolled environments such as changes in pose, facial expression and illumination. However, the feature based methods address the issues of face recognition in uncontrolled environments to a certain extent. Therefore, the biometric researchers are trying to devise the face recognition methods that perform optimally in uncontrolled environments. This paper presents a novel method of face recognition in uncontrolled environments that works with local binary pattern of facial images and compute the dissimilarity among them using Bray Curtis dissimilarity metric. A novel concept of filtering the LBP surface texture is developed. The proposed method we called as "augmented local binary" pattern ((ALBP)) works on a combination of the principle of locality of uniform and non-uniform patterns. It replaces non-uniform patterns with the majority value of uniform patterns and combined with neighboring uniform patterns to extract valuable information regarding local descriptors. The proposed method is tested on different databases consisting uncontrolled facial images such as extended Yale B, Yale A and our database. The experimental results show that the proposed method performs better at recognizing faces in uncontrolled environments such as the facial expression, illumination, and mild pose changes.

关键词： Face recognition local binary pattern Bray Curtis dissimilarity metric descriptor

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