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Architectural Style Classification Based on feature extraction module

IEEE ACCESS 2018年 6卷 52598-52606页

作者： Zhao, Peipei Miao, Qiguang Song, Jianfeng Qi, Yutao Liu, Ruyi Ge, Daohui Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China Xian Key Lab Big Data & Intelligent Vis Xian 710071 Shaanxi Peoples R China

Standard classification tasks have already achieved good results in computer vision. However, the task of Architectural style classification yet faces many challenges, since the rich inter-class relationships between different styles may disturb the classification accuracy. To better classify buildings, we propose a feature extraction module based on image preprocessed with Deformable Part-based Models (DPM). Specifically, we first use DPM to remove elements that are not related to classification, and capture representative elements of buildings, and then these elements are sent to our feature extraction module. In our feature extraction module, we adopt our improved ensemble projection method to maximize the inter-class distance and minimize the intra-class distance to find the common features in the same style and differences among different styles. Finally, the performances of several classifiers are tested and the best one of SVM classifier is selected to output the ultimate accuracy. Experimental results show that our approach achieves promising performance and is superior to previous methods.

关键词： Architectural style classification deformable part-based Models (DPM) feature extraction module improved ensemble projection (IEP) SVM classifier

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FEPA-Net: A Building extraction Network Based on Fusing the feature extraction and Position Attention module

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APPLIED SCIENCES-BASEL 2025年第8期15卷 4432-4432页

作者： Liu, Yuexin Duan, Yulin Zhang, Xuya Zhang, Wen Wang, Chang Univ Sci & Technol Liaoning Sch Civil Engn Anshan 114000 Peoples R China Chinese Acad Agr Sci Inst Agr Resources & Reg Planning State Key Lab Efficient Utilizat Arid & Semiarid A Beijing 100081 Peoples R China Chinese Acad Agr Sci Inst Agr Resources & Reg Planning Key Lab Agr Remote Sensing Minist Agr & Rural Affairs Beijing 100081 Peoples R China Chinese Acad Environm Planning Inst Strateg Planning Beijing 100041 Peoples R China Beijing Normal Univ Fac Geog Sci State Key Lab Earth Surface Proc & Resource Ecol Beijing 100875 Peoples R China

The extraction of buildings from remote sensing images is of crucial significance in urban management and planning, but it remains difficult to automatically extract buildings with precise boundaries from remote sensing images. In this paper, we propose the FEPA-Net network model, which integrates the feature extraction and position attention module for the extraction of buildings in remote sensing images. The suggested model is implemented by employing U-Net as a base model. Firstly, the number of convolutional operations in the model was increased to extract more abstract features of the objects on the ground;secondly, within the network, the ordinary convolution is substituted with the dilated convolution. This substitution aims to broaden the receptive field, with the primary intention of enabling the output of each convolution layer to incorporate a broader spectrum of feature information. Additionally, a feature extraction module is added to mitigate the loss of detailed features. Finally, the position attention module is introduced to obtain more context information. The model undergoes validation and analysis using the Massachusetts dataset and the WHU dataset. The experimental results demonstrate that the FEPA-Net model outperforms other comparative methods in quantitative evaluation. Specifically, compared to the U-Net model, the average cross-merge ratio on the two datasets improves by 1.41% and 1.43%, respectively. The comparison of the results shows that the FEPA-Net model effectively improves the accuracy of building extraction, reduces the phenomenon of wrong detection and omission, and can more clearly identify the building outline.

关键词： building extraction dilated convolution feature extraction module position attention module

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Concurrent attentional reconstruction network for 3D point cloud reconstruction from single image

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APPLIED SOFT COMPUTING 2025年 172卷

作者： Velagapalli, Premalatha Parveen, Nikhat Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Guntur 522302 Andhra Pradesh India Univ Bisha Dept Informat Sci POB 551 Bisha Saudi Arabia

The reconstruction of point clouds from a 2D single image is challenging due to the complex nature of images that cannot be projected easily. Also, the images include multiple objects that require huge concentration while transforming them into a point cloud representation. Deep learning-based methods are recently gaining attention due to the high-performance outcomes enlisted in the image processing domain. This paper introduces a two- tiered deep learning-based reconstruction model known as concurrent attentional reconstruction network (CARN) to better reconstruct a 3D point cloud from a 2D single image. The feature extraction and the point cloud prediction are the two modules employed in the proposed CARN model. Here, concurrent excited DenseNet (An important development in the field of computer vision is the use of dense convolutional neural networks (DenseNets) linked with concurrent squeeze-and-excitation (CSE) modules for feature extraction in 3D point cloud reconstruction from a single image. The attention-dense gated recurrent unit (AD-GRU) is the next module employed for point cloud reconstruction. The proposed CARN model is trained and tested with publicly available ShapeNet datasets. The Python platform is applied for implementation, and various performance metrics such as accuracy, Earth Mover's Distance (EMD), and Chamfer Distance (CD) are analyzed with existing methods. The proposed CARN model acquires an overall accuracy above 99 % and obtains minimum CD and EMD values for the ShapeNet dataset.

关键词： Reconstruction network feature extraction module Concurrent squeeze and excitation layer Gated recurrent unit Two-tiered deep learning Point cloud reconstruction and prediction

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Learning a Deep Metric:A Lightweight Relation Network for Loop Closure in Complex Industrial Scenarios

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Chinese Journal of Electronics 2021年第1期30卷 45-54页

作者： JIN Sheng CHEN Liang GAO Yu SHEN Changqing SUN Rongchuan School of Mechanical and Electric Engineering Soochow University School of Rail Transportation Soochow University

The similarity metric in Loop closure detection(LCD) is still considered in an old fashioned way, i.e. to pre-define a fixed distance function, leading to a limited performance. This paper proposes a general framework named LRN-LCD, i.e. a Lightweight relation network for LCD, which combines the feature extraction module and similarity metric module into a simple and lightweight network. The LRN-LCD, an end-to-end framework, can learn a non-linear deep similarity metric to detect loop closures from different scenes. Moreover, the LRN-LCD supports image sequences as input to speed up the similarity metric in real-time applications. Extensive experiments on several open datasets illustrate that LRN-LCD is more robust to strong condition variations and viewpoint variations than the mainstream methods.

关键词： lightweight relation network image sequences similarity metric feature extraction module SLAM (robots) loop closure detection robot vision nonlinear deep similarity loop closures fixed distance function learning (artificial intelligence) LRN-LCD framework feature extraction deep metric

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Detecting Deepfake Videos using Spatiotemporal Trident Network

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2024年第11期20卷 1-20页

作者： Lin, Kaihan Han, Weihong Li, Shudong Gu, Zhaoquan Zhao, Huimin Mei, Yangyang Guangzhou Univ Cyberspace Inst Adv Technol Guangzhou 510006 Peoples R China Peng Cheng Lab Dept New Networks Shenzhen 518055 Peoples R China Guangzhou Univ Cyberspace Inst Adv Technol Dept New Networks Peng Cheng Lab Guangzhou 510006 Peoples R China Harbin Inst Technol Shenzhen Sch Comp Sci & Technol Dept New Networks Peng Cheng Lab Shenzhen 518055 Peoples R China Guangdong Polytech Normal Univ Sch Comp Sci Guangzhou 510665 Peoples R China

The widespread dissemination of Deepfake in social networks has posed serious security risks, thus necessitating the development of an effective Deepfake detection technique. Currently, video-based detectors have not been explored as extensively as image-based detectors. Most existing video-based methods only consider temporal features without combining spatial features, and do not mine deeper-level subtle forgeries, resulting in limited detection performance. In this paper, a novel spatiotemporal trident network (STN) is proposed to detect both spatial and temporal inconsistencies of Deepfake videos. Since there is a large amount of redundant information in Deepfake video frames, we introduce convolutional block attention module (CBAM) on the basis of the I3D network and optimize the structure to make the network better focus on the meaningful information of the input video. Aiming at the defects in the deeper-level subtle forgeries, we designed three feature extraction modules (FEMs) of RGB, optical flow, and noise to further extract deeper video frame information. Extensive experiments on several well-known datasets demonstrate that our method has promising performance, surpassing several state-of-the-art Deepfake video detection methods.

关键词： Deepfake spatiotemporal trident networks feature extraction module

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The Musite open-source framework for phosphorylation-site prediction

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BMC BIOINFORMATICS 2010年第s12期11卷 S9-S9页

作者： Gao, Jianjiong Xu, Dong Univ Missouri Dept Comp Sci CS Bond Life Sci Ctr Columbia MO 65211 USA

Background: With the rapid accumulation of phosphoproteomics data, phosphorylation-site prediction is becoming an increasingly active research area. More than a dozen phosphorylation-site prediction tools have been released in the past decade. However, there is currently no open-source framework specifically designed for phosphorylation-site prediction except Musite. Results: Here we present the Musite open-source framework for building applications to perform machine learning based phosphorylation-site prediction. Musite was implemented with six modules loosely coupled with each other. With its well-designed Java application programming interface (API), Musite can be easily extended to integrate various sources of biological evidence for phosphorylation-site prediction. Conclusions: Released under the GNU GPL open source license, Musite provides an open and extensible framework for phosphorylation-site prediction. The software with its source code is available at http://***.

关键词： Phosphorylation Site Application Programming Interface Machine Learning Problem feature extraction module Phosphorylation Data

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A micro-expression recognition algorithm based on feature enhancement and attention mechanisms

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VIRTUAL REALITY 2023年第3期27卷 2405-2416页

作者： Tang, Mingwei Ling, Miaogui Tang, Jinglin Hu, Jie Xihua Univ Sch Comp & Software Engn Chengdu 610039 Peoples R China Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Sichuan Peoples R China

In recent years, facial micro-expression recognition based on deep learning technology has become an important research hotspot. These techniques have been used by researchers in psychology, computer vision, and security to make breakthroughs. However, the proposed algorithm for micro-expression recognition faces some challenges in practical application. For example, the VGGMag method uses only one feature for micro-expression recognition, while the network cannot extract more useful features. The STRCN method does not focus on the key parts of facial micro-expressions, so that all parts of the face contribute equally to the micro-expression recognition model. A micro-expression recognition algorithm based on feature enhancement and attention mechanisms (FEAM) is proposed to further improve the recognition rate of micro-expressions. Firstly, the FEAM method designs a feature extraction module to extract micro-expression features and LBP image features, which preserves the texture information of the face. Secondly, the network constructs a feature augmentation module to highlight facial landmark points of faces as a way to increase the surrounding pixel weights. And then, the model designs the attention module to generate the attention correlation matrix of LBP and enhanced feature maps using the attention mechanism, which allows the network to pay more attention to the changes of features around the eyes and mouth of the face. Finally, the feature maps are input to the classification module for recognition. FEAM method is evaluated on three micro-expression datasets, and advanced results are obtained. The experimental results show that the proposed model FEAM can achieve state-of-the-art results compared to other models.

关键词： Micro-expression recognition feature enhancement Attention mechanism feature extraction module Local binary pattern

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Motion Generation Based on Reliable Predictability using Self-organized Object features

Motion Generation Based on Reliable Predictability using Sel...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Nishide, Shun Ogata, Tetsuya Tani, Jun Takahashi, Toru Komatani, Kazunori Okuno, Hiroshi G. Kyoto Univ Dept Intelligence Sci & Technol Grad Sch Informat Kyoto Japan RIKEN Brain Sci Inst Saitama Japan

ISBN: (纸本)9781424466757

Predictability is an important factor for determining robot motions. This paper presents a model to generate robot motions based on reliable predictability evaluated through a dynamics learning model which self-organizes object features. The model is composed of a dynamics learning module, namely Recurrent Neural Network with Parametric Bias (RNNPB), and a hierarchical neural network as a feature extraction module. The model inputs raw object images and robot motions. Through bi-directional training of the two models, object features which describe the object motion are self-organized in the output of the hierarchical neural network, which is linked to the input of RNNPB. After training, the model searches for the robot motion with high reliable predictability of object motion. Experiments were performed with the robot's pushing motion with a variety of objects to generate sliding, falling over, bouncing, and rolling motions. For objects with single motion possibility, the robot tended to generate motions that induce the object motion. For objects with two motion possibilities, the robot evenly generated motions that induce the two object motions.

关键词： dynamics learning model feature extraction feature extraction module hierarchical neural network image motion analysis intelligent robots learning (artificial intelligence) motion control motion generation parametric bias radial basis function networks recurrent neural network reliability reliable predictability evaluation robot dynamics robot motions robot vision self-organized object features

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An integrated telemedicine platform for the assessment of affective physiological states

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DIAGNOSTIC PATHOLOGY 2006年第1期1卷 16-16页

作者： Katsis, Christos D. Ganiatsas, George Fotiadis, Dimitrios I. Univ Ioannina Dept Comp Sci Unit Med Technol & Intelligent Informat Syst GR-45110 Ioannina Greece Univ Ioannina Sch Med Dept Med Phys GR-45110 Ioannina Greece

AUBADE is an integrated platform built for the affective assessment of individuals. The system performs evaluation of the emotional state by classifying vectors of features extracted from: facial Electromyogram, Respiration, Electrodermal Activity and Electrocardiogram. The AUBADE system consists of: (a) a multisensorial wearable, (b) a data acquisition and wireless communication module, (c) a feature extraction module, (d) a 3D facial animation module which is used for the projection of the obtained data through a generic 3D face model;whereas the end-user will be able to view the facial expression of the subject in real time, (e) an intelligent emotion recognition module, and (f) the AUBADE databases where the acquired signals along with the subject's animation videos are saved. The system is designed to be applied to human subjects operating under extreme stress conditions, in particular car racing drivers, and also to patients suffering from neurological and psychological disorders. AUBADE's classification accuracy into five predefined emotional classes (high stress, low stress, disappointment, euphoria and neutral face) is 86.0%. The pilot system applications and components are being tested and evaluated on Maserati's car. racing drivers.

关键词： Facial Expression Emotion Recognition Electrodermal Activity feature extraction module Animation Video

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