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binary Image Classification: A Genetic Programming Approach to the Problem of Limited Training Instances

EVOLUTIONARY COMPUTATION 2016年第1期24卷 143-182页

作者： Al-Sahaf, Harith Zhang, Mengjie Johnston, Mark Victoria Univ Wellington Sch Engn & Comp Sci POB 600 Wellington 6140 New Zealand Victoria Univ Wellington Sch Math Stat & Operat Res POB 600 Wellington 6140 New Zealand

In the computer vision and pattern recognition fields, image classification represents an important yet difficult task. It is a challenge to build effective computer models to replicate the remarkable ability of the human visual system, which relies on only one or a few instances to learn a completely new class or an object of a class. Recently we proposed two genetic programming (GP) methods, one-shot GP and compound-GP, that aim to evolve a program for the task of binary classification in images. The two methods are designed to use only one or a few instances per class to evolve the model. In this study, we investigate these two methods in terms of performance, robustness, and complexity of the evolved programs. We use ten data sets that vary in difficulty to evaluate these two methods. We also compare them with two other GP and six non-GP methods. The results show that one-shot GP and compound-GP outperform or achieve results comparable to competitor methods. Moreover, the features extracted by these two methods improve the performance of other classifiers with handcrafted features and those extracted by a recently developed GP-based method in most cases.

关键词： Genetic programming local binary patterns one-shot learning image classification

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Classification of medicinal plants: An approach using modified LBP with symbolic representation

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NEUROCOMPUTING 2016年第Part3期173卷 1789-1797页

作者： Naresh, Y. G. Nagendraswamy, H. S. Univ Mysore Dept Studies Comp Sci Mysore Karnataka India

In this work, a symbolic approach for classification of plant leaves based on texture features is proposed. Modified local binary patterns (MLBP) is proposed to extract texture features from plant leaves. Texture of plant leaves belonging to same plant species may vary due to maturity levels, acquisition and environmental conditions. Hence, the concept of clustering is used to choose multiple class representatives and the intra-cluster variations are captured using interval valued type symbolic features. The classification is facilitated using a simple neatest neighbor classifier. Extensive experiments have been carried out on newly created UoM Medicinal Plant Dataset as well as publically available Flavia, Foliage and Swedish plant leaf datasets. Results obtained by proposed methodology are compared with the contemporary methodologies. The Outex dataset is also considered for experiments and the results are promising even on this synthetic dataset. (C) 2015 Elsevier B.V. All rights reserved.

关键词： local binary patterns Plant recognition Texture classification Symbolic representation

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Multilayer descriptors for medical image classification

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COMPUTERS IN BIOLOGY AND MEDICINE 2016年 72卷 239-247页

作者： Lumini, Alessandra Nanni, Loris Brahnam, Sheryl Univ Bologna DISI Via Sacchi 3 I-47521 Cesena Italy Dept Informat Engn Via Gradenigo 6-A I-35131 Padua Italy Missouri State Univ Comp Informat Syst 901 S Natl Springfield MO 65804 USA

In this paper, we propose a new method for improving the performance of 2D descriptors by building an n-layer image using different preprocessing approaches from which multilayer descriptors are extracted and used as feature vectors for training a Support Vector Machine. The different preprocessing approaches are used to build different n-layer images (n=3, n=5, etc.). We test both color and gray-level images, two well-known texture descriptors (local Phase Quantization and local binary Pattern), and three of their variants suited for n-layer images (Volume local Phase Quantization, local Phase Quantization Three-Orthogonal-Planes, and Volume local binary patterns). Our results show that multilayers and texture descriptors can be combined to outperform the standard single-layer approaches. Experiments on 10 datasets demonstrate the generalizability of the proposed descriptors. Most of these datasets are medical, but in each case the images are very different. Two datasets are completely unrelated to medicine and are included to demonstrate the discriminative power of the proposed descriptors across very different image recognition tasks. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Texture descriptors Ensemble local binary patterns local phase quantization Support vector machine Multilayer descriptors

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LBP-Based Segmentation of Defocus Blur

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2016年第4期25卷 1626-1638页

作者： Yi, Xin Eramian, Mark Univ Saskatchewan Dept Comp Sci Saskatoon SK S7N 5E2 Canada

Defocus blur is extremely common in images captured using optical imaging systems. It may be undesirable, but may also be an intentional artistic effect, thus it can either enhance or inhibit our visual perception of the image scene. For tasks, such as image restoration and object recognition, one might want to segment a partially blurred image into blurred and non-blurred regions. In this paper, we propose a sharpness metric based on local binary patterns and a robust segmentation algorithm to separate in-and out-of-focus image regions. The proposed sharpness metric exploits the observation that most local image patches in blurry regions have significantly fewer of certain local binary patterns compared with those in sharp regions. Using this metric together with image matting and multiscale inference, we obtained high-quality sharpness maps. Tests on hundreds of partially blurred images were used to evaluate our blur segmentation algorithm and six comparator methods. The results show that our algorithm achieves comparative segmentation results with the state of the art and have big speed advantage over the others.

关键词： Defocus blur segmentation LBP local binary patterns image restoration object recognition out-of-focus blurred

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LBP-Ferns-Based Feature Extraction for Robust Facial Recognition

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2016年第4期62卷 446-453页

作者： Jung, June-Young Kim, Seung-Wook Yoo, Cheol-Hwan Park, Won-Jae Ko, Sung-Jea Korea Univ Sch Elect Engn Dept 145 Anam Ro Seoul 02841 South Korea

Most facial recognition (FR) systems first extract discriminative features from a facial image and then perform classification. This paper proposes a method aimed at representing human facial traits and a low-dimensional feature extraction method using orthogonal linear discriminant analysis (OLDA). The proposed feature relies on a local binary pattern to represent texture information and random ferns to build a structural model. By concatenating its feature vectors, the proposed method achieves a high-dimensional descriptor of the input facial image. In general, the feature dimension is highly related to its discriminative ability. However, higher dimensionality is more complex to compute. Thus, dimensionality reduction is essential for practical FR applications. OLDA is employed to reduce the dimension of the extracted features and improve discriminative performance. With a representative FR database, the proposed method demonstrates a higher recognition rate and low computational complexity compared to existing FR methods. In addition, with a facial image database with disguises, the proposed algorithm demonstrates outstanding performance(1).

关键词： Facial recognition feature extraction local binary patterns random-ferns orthogonal linear discriminant analysis

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Weighted sparse representation for human ear recognition based on local descriptor

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JOURNAL OF ELECTRONIC IMAGING 2016年第1期25卷 1-12页

作者： Mawloud, Guermoui Djamel, Melaab Univ Batna Dept Elect Fac Engn Rue Chahid Mohamed El Hadi Boukhlouf Batna 05000 Algeria

A two-stage ear recognition framework is presented where two local descriptors and a sparse representation algorithm are combined. In a first stage, the algorithm proceeds by deducing a subset of the closest training neighbors to the test ear sample. The selection is based on the K-nearest neighbors classifier in the pattern of oriented edge magnitude feature space. In a second phase, the co-occurrence of adjacent local binary pattern features are extracted from the preselected subset and combined to form a dictionary. Afterward, sparse representation classifier is employed on the developed dictionary in order to infer the closest element to the test sample. Thus, by splitting up the ear image into a number of segments and applying the described recognition routine on each of them, the algorithm finalizes by attributing a final class label based on majority voting over the individual labels pointed out by each segment. Experimental results demonstrate the effectiveness as well as the robustness of the proposed scheme over leading state-of-the-art methods. Especially when the ear image is occluded, the proposed algorithm exhibits a great robustness and reaches the recognition performances outlined in the state of the art. (C) 2016 SPIE and IS&T

关键词： biometrics ear recognition pattern of oriented edge magnitude local binary patterns weighted sparse representation

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Recognizing spontaneous micro-expression from eye region

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NEUROCOMPUTING 2016年 217卷 27-36页

作者： Duan, Xiaodong Dai, Qiguo Wang, Xinhan Wang, Yuangang Hua, Zhichao Dalian Minzu Univ Dalian Key Lab Digital Technol Natl Culture Dalian 116600 Peoples R China Dalian Minzu Univ Sch Comp Sci & Engn Dalian 116600 Peoples R China

Micro-expression is a kind of spontaneous facial expression, which is with short duration and low intensity. Because of its involuntary feature, it is helpful to reveal one's true emotion when someone tries to conceal. Therefore, it has attracted a great of attentions from the field of affective computing. Previous methods focus on recognizing micro-expression on the whole face. In fact, it is worthy to note that micro-expression often appears in the eye area. In this paper, we present a framework to recognize micro-expressions within the eye region, namely eyeME. Specifically, the LBP-TOP feature is extracted from the eye region, and multiple classifiers are trained to recognize the expressions. We test the proposed framework on the widely used CASME2 database. The experimental results showed that the proposed eyeME framework performs better than the methods using the whole face and mouth region when identifying happy and disgust expressions. It confirmed that the information on eye region is critical to the recognition of these kinds of micro-expressions. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Micro-expression recognition Eye local binary patterns Spontaneous expression

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Weber Binarized Statistical Image Features (WBSIF) based video copy detection

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2016年第Jan.期34卷 50-64页

作者： Boukhari, Aissa Serir, Amina USTHB Lab Image Proc & Radiat Fac Elect & Comp BP 32 El Alia Algiers 16111 Algeria

This paper presents a new video copy detection system based on testing similarities between textural feature vectors which are extracted from videos. Herein, the proposed method is based on Weber Binarized Statistical Image Features (WBSIF) which is an interior improvement of Weber local Descriptor (WLD). Actually, the orientation gradient in WLD is substituted by a recent Binarized Statistical Image Features (BSIF) as a local textural descriptor. The WBSIF approach is tested on three databases and evaluated through several attacks. Moreover, the proposed method is compared to the recent existing approaches, especially those mostly used in the literature, which are based on the binary pattern descriptors. The obtained results outline the robustness and the effectiveness of the proposed video copy detection system in terms of precision, recall, Fscore, accuracy and collision test. This study shows clearly a noteworthy performance of the proposed scheme against currently existing techniques. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Video copy detection Video fingerprinting Content-based copy detection Key-frame local binary patterns Weber local descriptor Binarized statistical image features Weber binarized statistical image features

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Enhanced LBP texture features from time frequency representations for acoustic scene classification

Enhanced LBP texture features from time frequency representa...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Shamsiah Abidin Roberto Togneri Ferdous Sohel School of Electrical Electronics and Computer Engineering The University of Western Australia Perth 6009 Australia School of Engineering and Information Technology Murdoch University Perth 6150 WA Australia

ISBN: (纸本)9781509041183

This paper introduces the use of local binary patterns (LBP) extracted from a time-frequency representation (TFR) for acoustic scene classification. As LBP provides a description of the global TFR texture we propose a novel zoning mechanism that provides a simple solution to extract spectrally relevant local features which better characterize the audio TFRs. To further improve the classification performance, we perform feature and score level fusion of the proposed LBP (with zoning) with histogram of gradients (HOG) of the TFR images. Our technique demonstrates an improved performance by achieving a classification accuracy of 95.2% using a fusion of time-frequency derived features.

关键词： acoustic scene local binary patterns feature extraction time-frequency analysis fusion

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Texture modelling with nested high-order Markov-Gibbs random fields

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COMPUTER VISION AND IMAGE UNDERSTANDING 2016年 143卷 120-134页

作者： Versteegen, Ralph Gimel'farb, Georgy Riddle, Patricia Univ Auckland Dept Comp Sci Auckland 1142 New Zealand

Currently, Markov-Gibbs random field (MGRF) image models which include high-order interactions are almost always built by modelling responses of a stack of local linear filters. Actual interaction structure is specified implicitly by the filter coefficients. In contrast, we learn an explicit high-order MGRF structure by considering the learning process in terms of general exponential family distributions nested over base models, so that potentials added later can build on previous ones. We relatively rapidly add new features by skipping over the costly optimisation of parameters. We introduce the use of local binary patterns as features in MGRF texture models, and generalise them by learning offsets to the surrounding pixels. These prove effective as high-order features, and are fast to compute. Several schemes for selecting high-order features by composition or search of a small subclass are compared. Additionally we present a simple modification of the maximum likelihood as a texture modelling specific objective function which aims to improve generalisation by local windowing of statistics. The proposed method was experimentally evaluated by learning high-order MGRF models for a broad selection of complex textures and then performing texture synthesis, and succeeded on much of the continuum from stochastic through irregularly structured to near-regular textures. Learning interaction structure is very beneficial for textures with large-scale structure, although those with complex irregular structure still provide difficulties. The texture models were also quantitatively evaluated on two tasks and found to be competitive with other works: grading of synthesised textures by a panel of observers;and comparison against several recent MGRF models by evaluation on a constrained inpainting task. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Texture synthesis and analysis High-order MRFs local binary patterns Structure learning

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