Article
Computer Science, Information Systems
Abdul Wahab Muzaffar, Farhan Riaz, Tarik Abuain, Waleed Abdel Karim Abu-Ain, Farhan Hussain, Muhammad Umar Farooq, Muhammad Ajmal Azad
Summary: In this study, a novel rotation and scale invariant texture classification method based on Gabor filters is proposed. These filters are designed to capture the visual content of the images by their impulse responses, which are sensitive to rotation and scaling. The proposed method rearranges the filter responses and calculates patterns after binarizing the responses based on a specific threshold. The effectiveness of the proposed feature extraction method is demonstrated through experiments on famous texture datasets, and it is shown to be more robust to noise compared to other state-of-the-art methods considered in the study.
Article
Chemistry, Analytical
Hequn Li, Qiong Wang, Ling Ling, Ziqi Lv, Yun Liu, Mingxing Jiao
Summary: Coal gangue image recognition using laser speckle images achieved high accuracy and stability in industrial environments, surpassing natural image recognition. The proposed method provides a new and reliable approach for accurate classification of coal and gangue in mines.
Article
Computer Science, Artificial Intelligence
Haythem Ghazouani
Summary: Emotion recognition is a challenging problem in pattern recognition field, as it relies on the quality of face representation and lacks universal features to accurately capture all emotions. Combining multiple features to enhance recognition rate faces issues such as information redundancy and high dimensionality. A genetic programming framework called GP-FER is proposed in this work to address these challenges and has demonstrated superior performance on various facial expression datasets.
APPLIED SOFT COMPUTING
(2021)
Article
Forestry
Zhengguang Wang, Zilong Zhuang, Ying Liu, Fenglong Ding, Min Tang
Summary: The study introduced machine vision technology and unsupervised learning technique to reduce labor costs and improve production efficiency through feature vector extraction and data dimension reduction for color classification. Texture recognition was achieved based on color classification, enhancing the quality stability of solid wood panels.
Article
Engineering, Biomedical
Ravinder Kaur, Mamta Juneja, A. K. Mandal
Summary: This study examines the use of machine learning techniques for classifying renal lesions on CT images, quantifying renal parenchyma tissues using different texture models. Dimensionality reduction techniques are then applied to identify discriminating features. The KNN classifier achieved a classification accuracy of 84% with 5-fold cross-validation.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Computer Science, Artificial Intelligence
Di Lu, Dapeng Wang, Kaiyu Zhang, Xiangyuan Zeng
Summary: In response to the long time-consuming and low accuracy of existing age estimation methods, a new approach is proposed. This approach first extracts texture features using Gabor wavelet transform and fuses them using a statistical histogram. Then, it uses an improved atomic search algorithm with a chaos mechanism for feature selection. Finally, a support vector machine is used for age group classification. Experimental results demonstrate the superiority of this method.
CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Joaquim de Moura, Placido L. Vidal, Jorge Novo, Jose Rouco, Manuel G. Penedo, Marcos Ortega
Summary: Optical coherence tomography is widely used in medical imaging, particularly for analyzing retinal diseases like Diabetic Macular Edema. This study utilizes machine learning techniques to conduct a comprehensive analysis of these accumulations and tissue lesions, aiming to better understand the factors affecting them.
PATTERN ANALYSIS AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Erdal Tasci, Aybars Ugur
Summary: With the increasing number of digital images, computer-aided classification of image types is widely used. The feature extraction and selection stages play a crucial role in improving classification performance. In this study, a novel pattern recognition framework combining diverse and large-scale handcrafted feature extraction methods and the selection stage is developed. Genetic algorithms are used for feature selection. Experimental results show high accuracy rates on different datasets, making the proposed method competitive with existing state-of-the-art methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Shao Liu, Jiaqi Yang, Sos S. Agaian, Changhe Yuan
Summary: The article introduces three novel features and a mature model structure for artistic movement recognition of portrait paintings, showing the successful application of these features in various neural networks through extensive evaluation. Additionally, a new portrait database containing 927 paintings from 6 different art movements is presented, demonstrating the superiority of the proposed method over state-of-the-art approaches.
IMAGE AND VISION COMPUTING
(2021)
Article
Computer Science, Information Systems
E. Rachdi, I. El Khadiri, Y. El merabet, Y. Rhazi, C. Meurie
Summary: This paper introduces a novel local feature extraction operator called MTSP, which is composed of two single-scale encoders, STP and SSP, designed based on a novel set theory pattern encoding scheme. Unlike other parametric texture operators, MTSP incorporates dynamic thresholds and can capture more detailed image information through the fusion of STP and SSP encoders. Experimental results demonstrate that MTSP achieves reliable performance stability on ten texture datasets and outperforms several representative methods in texture modeling, as verified by statistical tests.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Xu Liang, Zhaoqun Li, Dandan Fan, Jinxing Li, Wei Jia, David Zhang
Summary: In this study, a large-scale touchless palmprint dataset with 2334 palms from 1167 individuals was built, and a novel deep learning framework called 3D convolution palmprint recognition network (3DCPN) was proposed for touchless palmprint recognition. By leveraging 3D convolutions and Gabor features, as well as a region-based loss function, the proposed method demonstrated superior performance in experiments.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Theory & Methods
Chol-Gyun Ri, Kwang-Il Ri, Jong-Hwan Ri
Summary: In this study, a robust and efficient finger vein recognition method is proposed, consisting of three stages: preprocessing, feature extraction, and comparison. The method shows state-of-the-art performance and efficiency in practical applications.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
Article
Computer Science, Information Systems
Frimpong Twum, Yaw Marfo Missah, Stephen Opoku Oppong, Najim Ussiph
Summary: Texture plays a crucial role in computer vision, and the Log Gabor filter is used to extract the texture features of medicinal plant leaves. The Log Gabor filter outperforms the Gabor filter in terms of classifier performance.
Article
Oncology
Pashmina Kandalgaonkar, Arpita Sahu, Ann Christy Saju, Akanksha Joshi, Abhishek Mahajan, Meenakshi Thakur, Ayushi Sahay, Sridhar Epari, Shwetabh Sinha, Archya Dasgupta, Abhishek Chatterjee, Prakash Shetty, Aliasgar Moiyadi, Jaiprakash Agarwal, Tejpal Gupta, Jayant S. Goda
Summary: This study used MRI texture features to classify IDH wild type and IDH mutant type high-grade gliomas, resulting in a predictive accuracy of 89% and an AUC of 0.89 using a Quadratic Support Vector Machine Classifier and a 10-fold internal cross-validation approach.
FRONTIERS IN ONCOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Mostafa Khojastehnazhand, Mozaffar Roostaei
Summary: This study used a machine vision system and texture feature extraction methods to classify seven varieties of wheat in the East Azerbaijan Province of Iran. By utilizing unsupervised and supervised methods, along with feature extraction, the different wheat varieties were identified with over 95% accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.