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IEEE International Conference on Multimedia and Expo

作者： Rushi Lan Yicong Zhou Yuan Yan Tang C. L. Philip Chen Department of Computer and Information Science University of Macau

ISBN: (纸本)9781479947607

Person reidentification is to identify the persons observed in nonoverlapping camera networks. Most existing methods usually extract features from the red, green, and blue color channels of images individually. They, however, neglect the connections between each color component in the image. To overcome this problem, a novel quaternionic local binary pattern (QLBP) is proposed for person reidentification in this paper. In the proposed QLBP, each pixel in a color image is represented by a quaternion so that we can handle all color components in a holistic way. A novel pseudo-rotation of quaternion (PRQ) is proposed to rank two quaternions. Some properties of PRQ are also discussed. After a QLBP coding, the local histograms are extracted and used as features. Experiments on two public benchmarking datasets, ETHZ and i-LIDS MCTS, are carried out to evaluate the QLBP performance. Comparison results show that the QLBP outperforms several stat-of-art methods for person reidentification.

关键词： Person reidentification Quaternion local binary pattern Feature extraction

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Multilevel hybrid accurate handcrafted model for myocardial infarction classification using ECG signals

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2023年第5期14卷 1651-1668页

作者： Barua, Prabal Datta Aydemir, Emrah Dogan, Sengul Kobat, Mehmet Ali Demir, Fahrettin Burak Baygin, Mehmet Tuncer, Turker Oh, Shu Lih Tan, Ru-San Acharya, U. Rajendra Univ Southern Queensland Sch Business Informat Syst Toowoomba Qld 4350 Australia Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW 2007 Australia Sakarya Univ Coll Management Dept Management Informat Sakarya Turkey Firat Univ Technol Fac Dept Digital Forens Engn Elazig Turkey Firat Univ Firat Univ Hosp Dept Cardiol TR-23119 Elazig Turkey Bandirma Onyedi Eylul Univ Fac Engn & Nat Sci Dept Software Engn Bandirma Turkey Ardahan Univ Coll Engn Dept Comp Engn Ardahan Turkey Ngee Ann Polytech Dept Elect & Comp Engn Singapore 599489 Singapore Natl Heart Ctr Singapore Dept Cardiol Singapore Singapore Duke NUS Med Sch Singapore Singapore SUSS Univ Sch Sci & Technol Dept Biomed Engn Singapore Singapore Asia Univ Dept Biomed Informat & Med Engn Taichung Taiwan

Myocardial infarction (MI) is detected using electrocardiography (ECG) signals. Machine learning (ML) models have been used for automated MI detection on ECG signals. Deep learning models generally yield high classification performance but are computationally intensive. We have developed a novel multilevel hybrid feature extraction-based classification model with low time complexity for MI classification. The study dataset comprising 12-lead ECGs belonging to one healthy and 10 MI classes were downloaded from a public ECG signal databank. The model architecture comprised multilevel hybrid feature extraction, iterative feature selection, classification, and iterative majority voting (IMV). In the hybrid handcrafted feature (HHF) generation phase, both textural and statistical feature extraction functions were used to extract features from ECG beats but only at a low level. A new pooling-based multilevel decomposition model was presented to enable them to create features at a high level. This model used average and maximum pooling to create decomposed signals. Using these pooling functions, an unbalanced tree was obtained. Therefore, this model was named multilevel unbalanced pooling tree transformation (MUPTT). On the feature extraction side, two extractors (functions) were used to generate both statistical and textural features. To generate statistical features, 20 commonly used moments were used. A new, improved symmetric binary pattern function was proposed to generate textural features. Both feature extractors were applied to the original MI signal and the decomposed signals generated by the MUPTT. The most valuable features from among the extracted feature vectors were selected using iterative neighborhood component analysis (INCA). In the classification phase, a one-dimensional nearest neighbor classifier with ten-fold cross-validation was used to obtain lead-wise results. The computed lead-wise results derived from all 12 leads of the same beat were input to t

关键词： local binary pattern Statistical feature extraction MI classification ECG signal processing

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DLSANet: Facial expression recognition with double-code LBP-layer spatial-attention network

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IET IMAGE PROCESSING 2023年第9期17卷 2659-2672页

作者： Guo, Xing Lu, Siyuan Wang, Shuihua Lu, Zhihai Zhang, Yudong Nanjing Normal Univ Sch Educ Sci Nanjing 210023 Jiangsu Peoples R China Univ Leicester Sch Comp & Math Sci Leicester LE1 7RH England Henan Polytech Univ Sch Comp Sci & Technol Jiaozuo Henan Peoples R China King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Syst Jeddah Saudi Arabia

Facial expression recognition (FER) is widely used in many fields. To further improve the accuracy of FER, this paper proposes a method based on double-code LBP-layer spatial-attention network (DLSANet). The backbone model for the DLSANet is an emotion network (ENet), which is modified with a double-code LBP (DLBP) layer and a spatial attention module. The DLBP layer is at the front of the first convolutional layer. More valuable features can be extracted by inputting the image processed by DLBP into convolutional layers. The JAFFE and CK+ datasets are used, which contain seven expressions: happiness, anger, disgust, neutral, fear, sadness, and surprise. The average of fivefold cross-validation shows that DLSANet achieves a recognition accuracy of 93.81% and 98.68% on the JAFFE and CK+ datasets. The experiment reveals that the DLSANet can produce better classification results than state-of-the-art methods.

关键词： artificial intelligence belief networks convolutional neural network local binary pattern pattern recognition spatial attention module

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A novel technique for classifying Parkinson's disease using structural MRI scans

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第29期82卷 46011-46036页

作者： Khanna, Ketna Gambhir, Sapna Gambhir, Mohit J C Bose Univ Sci & Technol YMCA Faridabad 121002 Haryana India Minist Educ Delhi 110001 India

Parkinson's is a movement-related progressive disorder that causes irreversible damage and heavily derails the patient's life. PD is generally detected via inspection of symptoms and family history by neurologists which may be inefficient, time-consuming and costly to the patient. Although the motor-related signs act as the major contributor to Parkinson's detection, these occur in the later stage of the disease when the symptoms have already intensified. Henceforth, there is a necessity for a Computer Aided Diagnosis (CAD) for an early yet accurate classification of Parkinson's. This work proposes a fusion of 3D- Discrete Wavelet Transform (DWT) and a variant of 3D local binary pattern (LBP) on 3D T1-weighted structural Magnetic Resonance Imaging (sMRI) scans acquired from two publically available databases namely: PPMI and SWADESH for Parkinson's diagnosis. The International dataset has been retrieved from the PPMI database and the Indian dataset has been acquired from SWADESH database. Features have been extracted from the gray matter. Fisher Discriminant Ratio (FDR) has been utilized for selecting relevant features. Support Vector Machine, Logistic Regression, Random forest and K-NN have been used for performing classification. The proposed technique achieved a performance accuracy of 92.45% and 90.57% for data acquired from the PPMI database and SWADESH database respectively. It outperformed the state-of-art techniques with respect to accuracy, sensitivity and specificity. From the results, it has been concluded that the suggested technique has the potential for an early yet effective identification of Parkinson's and can be utilized for clinical diagnosis by neurologists.

关键词： Parkinson's disease Machine learning Magnetic resonance imaging Wavelet transform Fisher discriminant ratio local binary pattern SVM Random forest

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An Evaluation of Effectiveness of a Texture Feature Based Computerized Diagnostic Model in Classifying the Ovarian Cyst as Benign and Malignant from Static 2D B-Mode Ultrasound Images

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CURRENT MEDICAL IMAGING 2023年第3期19卷 292-305页

作者： Sheela, S. Sumathi, Manickam Sathyabama Inst Sci & Technol Dept Elect & Commun Engn Chennai 600119 India Rajalakshmi Inst Technol Dept Elect & Commun Engn Chennai 600124 India

Objective: To develop a computerized diagnostic model to characterize the ovarian cyst at its early stage in order to avoid unnecessary biopsy and patient anxiety. Background: The main cause of mortality and infertility in women is ovarian cancer. It is very difficult to diagnose ovarian cancer using ultrasonography as benign and malignant ovarian masses or cysts exhibit similar characteristics. Early prediction and characterization of ovarian masses will reduce the unwanted growth of the ovarian mass. Materials and Methods: Transvaginal 2D B mode ovarian mass ultrasound images were preprocessed initially to enhance the image quality. And then, the region of interest (ROI) in this case ovarian cyst was segmented. Finally, local binary pattern (LBP) textural features were extracted. A Support Vector Machine was trained to classify the ovarian cyst or mass as benign or malignant. Results: The performance of the SVM improved with an average accuracy of 92% when the textural features were extracted from the Original Gray Value-based LBP (OGV-LBP) image than the histogram-based LBP. Conclusion: The SVM can classify the transvaginal 2D B mode ovarian cyst ultrasound images into benign and malignant effectively when the textural features from the original gray value-based LBP extracted were considered.

关键词： Ovarian cancer feature extraction SVM classifier local binary pattern texture feature extraction segmentation ovarian cyst

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FLA-Net: multi-stage modular network for low-light image enhancement

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VISUAL COMPUTER 2023年第4期39卷 1251-1270页

作者： Yu, Nana Li, Jinjiang Hua, Zhen Shandong Technol & Business Univ ICT Yantai Sch Informat & Elect Engn Inst Network Technol Yantai Peoples R China Shandong Technol & Business Univ Sch Comp Sci & Technol Yantai Peoples R China

Under the condition of low illumination, the image is easy to appear unclear, the contrast is not enough, and the details can not be fully displayed, which will inevitably bring huge obstacles to the computer vision task. However, low-light image enhancement is a challenging task. Therefore, we design a multi-stage modular network FLA-Net (F: Feature Aggregation Module, L: LBP Module, A: Attention Module) for low-light image enhancement. In this study, we divide the task into three stages: the first stage is the feature extraction stage (FE stage), in which LBP module is added to the feature extraction stage to help the network better recover the image texture details and other information;the second stage is the feature aggregation stage (FA stage), in which the feature aggregation module is added, which can help the network integrate deeper contrast information and color information;the third stage is the image enhancement stage (IE stage), in which the channel attention module is added to the image enhancement stage to make the network pay more attention to the dark areas of the image, which is conducive to the enhancement and recovery of low-light images. Our FLA-Net method solves a series of problems existing in the existing low-light enhancement methods. In addition, we have verified the effectiveness of FLA-Net on multiple public datasets and compared with multiple existing methods for image enhancement. A large number of experimental results show that the enhanced image generated by our method has better subjective visual quality and is better than the most advanced low-light enhancement method in several objective evaluation indicators.

关键词： Image enhancement local binary pattern Feature aggregation module Attention mechanism

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A hybrid classification method with dual-channel CNN and KELM for hyperspectral remote sensing images

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2023年第1期44卷 289-310页

作者： Zhao, Huimin Wang, Chenchen Chen, Huayue Chen, Tao Deng, Wu Civil Aviat Univ China Sch Elect Informat & Automat Tianjin Peoples R China China West Normal Univ Sch Comp Sci Nanchong 637002 Peoples R China

In this paper, a hybrid classification method based on dual-channel convolutional neural network (DC-CNN) and kernel extreme learning machine (KELM), namely PLDC-KELM, is proposed to improve the spatial-spectral feature extraction ability and hyperspectral remote sensing image (HRSI) classification accuracy. In the proposed PLDC-KELM, principal component analysis (PCA) is employed to reduce the dimensionality of original HRSI, and local binary pattern (LBP) is utilized to extract spatial features from the data after dimensionality reduction, and the one-dimensional convolutional neural network (1D-CNN) model is constructed to extract deep spatial features from the dimensionality-reduced spatial features. The secure connection layers of two 1D-CNN models are connected to fuse deep spectral and spatial features. Finally, the fused features are input into KELM in order to realize an HRSI classification method. Indian Pines data, Pavia University data, and Salinas data are used to prove the effectiveness of the PLDC-KELM. The experimental results show that the PLDC-KELM method can achieve classification accuracy of 99.95%, 99.98%, and 99.97%, respectively. It is an effective method for hyperspectral remote sensing image classification.

关键词： hyperspectral remote sensing image classification dual-channel convolutional neural network principal component analysis local binary pattern kernel extreme learning machine

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Symmetric mean binary pattern-based Pakistan sign language recognition using multiclass support vector machines

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NEURAL COMPUTING & APPLICATIONS 2023年第1期35卷 949-972页

作者： Shah, Syed Muhammad Saqlain Khan, Javed, I Abbas, Syed Husnain Ghani, Anwar Int Islamic Univ Dept Comp Sci & Software Engn Islamabad Pakistan Kent State Univ Dept Comp Sci Kent OH 44242 USA

Sign languages are a fundamental source of communication for the deaf community, generated through movements of the human body. Similar to the natural languages, sign languages vary from region to region, and from nation to nation. Pakistan sign language (PSL) is inspired by Urdu, national language of Pakistan. It has 38 alphabet signs, and out of them, 36 are represented by the static hand gestures. Automatic recognition of a sign language helps in interaction between the hearing and deaf individuals. The number of quality efforts in context of Pakistan sign language recognition is quite limited, leaving a fair room for addressing open issues of research, i.e., (i) efficient hand detection under complex backgrounds and (ii) extracting signer independent feature vector that should not only be discriminant for all the PSL alphabets but reduced dimension as well. In this research, recognition of PSL static alphabets is addressed where the task of hand localization is accomplished through faster regional-convolutional neural networks (faster R-CNN). Feature extraction is achieved through presenting symmetric mean-based binary patterns (sMBP) that extend uniform local binary patterns. The proposed feature vector not only suppresses the noise but preserves the rotation invariance as well. Classification task is accomplished through error correction output codes (ECOC)-based support vector machines using linear, polynomial and radial basis function kernels with one-vs-one and one-vs-all modalities. The proposed technique is validated through PSL dataset, created by seven native signers, having 7174 images. The comparative results clearly demonstrate the authority of the proposed technique over all of its baseline and competitor techniques.

关键词： Pakistan sign language Faster R-CNN Support vector machines local binary pattern

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Comparative analysis of features extraction techniques for black face age estimation

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AI & SOCIETY 2024年第4期39卷 1769-1783页

作者： Oladipo, Oluwasegun Omidiora, Elijah Olusayo Osamor, Victor Chukwudi Covenant Univ Dept Comp & Informat Sci Ota Ogun State Nigeria Ladoke Akintola Univ Technol LAUTECH Dept Comp Sci & Engn Ogbomosho Oyo State Nigeria

A computer-based age estimation is a technique that predicts an individual's age based on visual traits derived by analyzing a 2D picture of the individual's face. Age estimation is critical for access control, e-government, and effective human-computer interaction. The other-race effect has the potential to cause techniques designed for white faces to underperform when used in a region with black faces. The outcome is the consequence of intermittent training with faces of the same race and the encoding structure of the trained face images, which is based on the feature extraction technique used. This study contributes to a constructive comparison of three feature-extraction techniques, namely, local binary pattern (LBP), Gabor Wavelet (GW), and wavelet transformation, used in the development of a genetic algorithm (GA)-artificial neural network (ANN)-based age estimation system. The feature extraction techniques used are proven to produce a wealth of shape and textural information. The GA-ANN constitutes the age classifier module. The correct classification rate was chosen as the performance metrics in this study. The results demonstrated that the LBP is a more robust representation of the black face than the GW and Wavelet transformations, as evidenced by its accuracy rate of 91.76 compared to 89.41 and 84.71 achieved with the GW and Wavelet transformation age estimation systems, respectively.

关键词： Features extraction Black face Age estimation local binary pattern Gabor wavelet Wavelet transformation

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Detection of Brain Tumor based on Features Fusion and Machine Learning

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 983-999页

作者： Amin, Javeria Sharif, Muhammad Raza, Mudassar Yasmin, Mussarat Department of Computer Science COMSATS University Islamabad Wah Campus Wah Cantt Pakistan

Automated detection of brain tumor is a more challenging work due to the variability and complexity of shape, size, texture and location of lesions. The non-invasive MRI methods appear as a front line brain tumor detection tools (without ionization radiation). In this manuscript, an unsupervised clustering approach for tumor segmentation is proposed. Moreover, a fused feature vector is used which is a mixture of Gabor wavelet features (GWF), histograms of oriented gradient (HOG), local binary pattern (LBP) and segmentation based fractal texture analysis (SFTA) features. Random forest (RF) classifier is applied for classification between three sub tumoral regions such as complete, enhancing and non-enhancing tumor. To avoid over fitting problem, fivefold and 0.5 holdout cross-validation approaches are used. The promising detection efficiency depicts the dominance of proposed approach. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： local binary pattern

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