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Advances in Visual Computing 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： George Bebis Richard Boyle Bahram Parvin Darko Koracin Paolo Remagnino Fatih Porikli Jörg Peters James Klosowski Laura Arns Yu Ka Chun theresa-Marie Rhyne Laura Monroe

ISBN: (数字)9783540896395

ISBN: (纸本)9783540896388

It is with greatpleasure that we present the proceedings of the 4th international symposium on Visual Computing (ISVC 2008) in Las Vegas, Nevada. ISVC o?ers a common umbrella for the four main areas of visual computing including vision, graphics, visualization, and virtual reality. Its goal is to provide a forum for researchers, scientists, engineers and practitioners throughout the world to present their latest research ?ndings, ideas, developments and applications in the broader area of visual computing. this year,ISVC grew signi?cantly; the programconsisted of 15 oralsessions, 1 poster session, 8 special tracks, and 6 keynote presentations. the response to the call for papers was very strong; we received over 340 submissions for the main symposium from which we accepted 102 papers for oral presentation and 70 papers for poster presentation. Special track papers were solicited separately through the Organizing and Program Committees of each track. A total of 56 papers were accepted for oral presentation and 8 papers for poster presentation in the special tracks. All papers were reviewed with an emphasis on potential to contribute to the state of the art in the ?eld. Selection criteria included accuracy and originality of ideas, clarity and signi?cance of results, and presentation quality. the review process was quite rigorous, involving two to three independent blind reviews followed by several days of discussion. During the discussion period we tried to correct anomalies and errors that might have existed in the initial reviews.

关键词： pattern recognition Programming Techniques Computer Applications image processing and Computer Vision Biometrics Artificial Intelligence

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Elevated Learning based Secured Phishing Website Identification Methodology using Artificial Intelligence Assistance

Elevated Learning based Secured Phishing Website Identificat...

引用

Electronics and Sustainable Communication Systems (ICESC), 2020 international Conference on

作者： G. Ramkumar Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Phishing attacks pose a huge threat for online security and therefore need advanced detecting techniques in order to mitigate the damages it causes. this paper introduces a sophisticated model for the detection of phishing websites, named the Blended ResNet-EfficientNet Model (BREM), which unifies the advantages of the ResNet and EfficientNet architectures. To address this challenge, BREM uses the rich hierarchical pattern recognition ability of ResNet-50 and the practical feature extraction capability of EfficientNet-B3 to achieve classification performance in phishing detection. On overall assessment BREM outperforms both traditional machine learning models and standalone deep learning model with an accuracy of 96%, precision at 94%, recall of 95% and FI score of 94.5%. these same authors validate the high specificity (97 %), negative predictive value (95 %) and Matthews correlation coefficient of 0.92 further underlining the robustness and reliability of BREM. this approach does not only improve the accuracy of detection, but also provides much better security against phishing campaigns. In the near future research directions such as real-time deployment, more experiments on different feature sets, adversarial robustness, transfer learning from heterogeneous datasets, model learning models and pattern recognition. they are able to draw from an immense amount of data and address minor deviances, which allows AI's better discrimination of real versus false websites, therefore increasing user cyber protections and preventable breaches [3] [4]. At the heart of securing phishing websites identification is making use of powerful AI capabilities to scan and recognize phishing convincingly. this AI utilizes different methods - natural language processing (NLP), image recognition and behavioral analysis - to keep an eye on things happening to web content and its design. Behavior analysis tracks user-interactions and web behavior to detect for abnormal operations, which co

关键词： Accuracy Phishing Scalability Transfer learning Robustness Natural language processing Real-time systems pattern recognition Security Protection

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Building Segmentation from Remote Sensing image via DWT Attention Networks 23

Building Segmentation from Remote Sensing Image via DWT Atte...

引用

5th international Conference on pattern recognition and Intelligent Systems, PRIS 2023

作者： Wu, Yingbin Zhou, Mingquan Geng, Shengling Zhang, Dan College of Computer Qinghai Normal University Xining China College of Math and Information Yuncheng University Yuncheng China State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China

ISBN: (纸本)9781450399968

the attention mechanism has been widely used and achieved good results in many visual tasks. But the calculations of attention mechanism in vision tasks consume huge spaces and times, which is the obvious disadvantage of this method. In order to alleviate this problem, we use the DWT(Discrete Wavelet Transform) method to reduce the complexity of attention calculation. DWT can transform an N-dimensional vector into two vectors, one is the low-frequency component of N/2 dimension and the other is high-frequency component of N/2 dimension too. We only use the low-frequency to calculate the attention matrixes, which can reduce the complexity of matrix multiplication, then the time and space consumption of the network is reduced significantly. We also find that the building segmentation in the remote sensing image is different from the other scene segmentation, that the sizes and numbers of different classes of the targets in the general scene images are obvious. Despite all this, our method is still applicable for the targets with large numbers and sizes in general scene images, but not for the targets with small sizes and numbers, and this view is also verified by the subsequent experiments on different datasets. We apply our method on three typical networks (Danet, Swin and Segmenter), and carry out comprehensive experiments on the Cityscape dataset and three building segmentation datasets (Inria Aerial Dataset, Massachusetts Buildings Dataset and Chinese Style Architecture Dataset). the experiments show that, our method is more suitable for building segmentation and can reduce the complexity of the model calculation in building segmentation, and the Mean IoU of segmentation results is not reduced clearly, some even improved. © 2023 ACM.

关键词： Remote sensing

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Cross-modal retrieval based on fusion lightweight network 22

Cross-modal retrieval based on fusion lightweight network

引用

Proceedings of the 2022 5th international Conference on Artificial Intelligence and pattern recognition

作者： Ying Liu Lu Chao Yingying Guo Jie Fang Weidong Zhang Center for Image and Information Processing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

With the increase in data diversity, how to solve the multiple semantic problems under modality becomes the key to cross-modal retrieval. Most of the existing cross-modal methods often accompany many parameters and large models, and they have low operation efficiency. In this paper, we propose a cross-modal retrieval method based on fusion lightweight networks to address the aforementioned problem. Our goal is to extract the features of different modalities separately through two lightweight networks. Additionally, we propose a fusion strategy for local and global features to better preserve local fine-grained information. Finally, we use the generative adversarial training to preserve the contextual semantic information between different modalities and improve the generalization ability of the model. Experiments show that the algorithm in this paper achieves excellent cross-modal retrieval performance on the MSCOCO dataset.

关键词： ALBERT, Adversarial Training Cross-Modal Retrieval, image-Text Matching

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BERT-based Multimodal Aspect-Level Sentiment Analysis for Social Media 22

BERT-based Multimodal Aspect-Level Sentiment Analysis for So...

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Proceedings of the 2022 5th international Conference on Artificial Intelligence and pattern recognition

作者： Zhe Wang Ying Liu Jianning Yang Center for Image and Information Processing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

Aspect-level sentiment analysis, a sub-task of sentiment analysis, aims to identify the sentiment polarity of a given aspect in a sentence. In recent years, with the diversification of the web, people are no longer satisfied with using text alone to post their status on social media, but also often use images as a way of recording, and the combination of images and text has gradually become an important form of data in the social media domain. However, aspect-based sentiment analysis is currently mostly applied to textual content, which neglects the role of image data in enhancing the robustness of text-based models. this paper proposes a multimodal sentiment analysis framework for social media based on the BERT model. the model is validated by importing text features and image features into the BERT model, which lends itself to extracting the interconnections between text and images. It is shown through experiments that the interaction effects between cross-modal data can be learnt by importing the BERT module, making social media multimodal sentiment classification better, and validating the effectiveness of the model.

关键词： Aspect-level sentiment analysis BERT Deep learning Multimodality Social media

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CBAM-DCE: A Non-Reference image Correction Algorithm for Uneven Illumination 22

CBAM-DCE: A Non-Reference Image Correction Algorithm for Une...

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Proceedings of the 2022 5th international Conference on Artificial Intelligence and pattern recognition

作者： Mengyu Fan Jin Lu Xianguang Kong Wei Sun Yijun Sun Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

Affected by the change in daytime illumination sequence and by the shooting angle in the complex field environment, the kiwifruit images possess the unfriendly features of uneven illumination, such as local darkness and local brightness. the ill-posed image with uneven illumination will seriously constraint the subsequent image analysis processing. Current deep learning methods have achieved satisfactory results, and a large number of paired images (one is the input image, one is the ground truth image) is required to train the better network performance. However, it is difficult to capture ground truth images of the kiwifruit in the field. Based on this, the paper proposed Convolutional Block Attention Module Deep Curve Estimation (CBAM-DCE) to accomplish a non-reference illumination unevenness correction for field kiwifruit images. A deep learning network model is used to estimate the image-specific curve for image enhancement, and a non-reference loss function is applied to evaluate the image enhancement effect. Compared with seven related enhancement algorithms, the presented algorithm shakes off uneven illumination or normal-light image pairs for training. Five different public datasets and our Kiwifruit dataset were used in the experiments. Experiments demonstrate that our proposed CBAM-DCE is superior to other state-of-the-art algorithms for enhancing natural images under different lighting conditions.

关键词： Deep learning Field complex environment Non-reference Uneven illumination correction

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A Method with Universal Transformer for Multimodal Sentiment Analysis 22

A Method with Universal Transformer for Multimodal Sentiment...

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Proceedings of the 2022 5th international Conference on Artificial Intelligence and pattern recognition

作者： Hao Ai Ying Liu Jie Fang Sheikh Faisal Rashid Center for Image and Information Processing Xi'an University of Posts and Telecommunications China Al-Khwarizmi Institute of Computer Science (KICS) University of Engineering & Technology (UET) Pakistan

ISBN: (纸本)9781450396899

Multimodal sentiment analysis refers to the use of computers to analyze and identify the emotions that people want to express through the extracted multimodal sentiment features, and it plays a significant role in human-computer interaction and financial market prediction. Most existing approaches to multimodal sentiment analysis use contextual information for modeling, and while this modeling approach can effectively capture the contextual connections within modalities, the correlations between modalities are often overlooked, and the correlations between modalities are also critical to the final recognition results in multimodal sentiment analysis. therefore, this paper proposes a multimodal sentiment analysis approach based on the universal transformer, a framework that uses the universal transformer to model the connections between multiple modalities while employing effective feature extraction methods to capture the contextual connections of individual modalities. We evaluated our proposed method on two benchmark datasets for multimodal sentiment analysis, CMU-MOSI and CMU-MOSEI, and the results outperformed other methods of the same type.

关键词： Emotional Features Feature Extraction Multimodal sentiment analysis Universal transformer

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Few-Shot image Classification Based on Cross-Dimensional Interactive Attention 22

Few-Shot Image Classification Based on Cross-Dimensional Int...

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Proceedings of the 2022 5th international Conference on Artificial Intelligence and pattern recognition

作者： Ying Liu Yilun Bai Hengchang Zhang Xin Che Center for Image and Information Processing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

In recent years, deep learning techniques have achieved great success in traditional image classification tasks, however, it is often difficult to achieve good results with a small amount of labeled data and prone to overfitting. therefore, scholars have started to focus on image classification methods based on few-shot learning. the prototype network uses the mean value of the support set samples as the prototype, and achieves classification by calculating the distance between the query set samples and the prototype. To enhance the feature representation capability of the prototype network, this paper proposes a few-shot image classification method based on cross-dimensional interactive attention. the algorithm uses the pre-trained model Resnet-12 to extract deep features of images and introduces the cross-dimensional interactive attention mechanism to link the information between channel and spatial dimensions through triple attention, which enhances the information interaction of each dimension. Meanwhile, in order to improve the problem of insufficient generalization ability of the prototype network, this algorithm uses a gradient-centered optimization algorithm to zero-mean the weight gradient, which improves the generalization ability of the network and improves the classification accuracy. Extensive experimental results show that the proposed algorithm performs well in the few-shot image classification task.

关键词： Cross-dimensional interactive attention Few-shot learning image classification Information interaction Prototype network

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Medical image Analysis Using Deep Learning Technnique 23

Medical Image Analysis Using Deep Learning Technnique

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Proceedings of the 5th international Conference on Information Management & Machine Intelligence

作者： Surabhi Saxena Nikhat Parveen Devendra Agarwal Department of Artificial Intelligence and Data Science Poornima Institute of Engineering & Technology India Department of Computer Science and Engineering Konneru Lakshmaiah Education Foundation India Computer Science Goel Instiute of Technology and Management India

ISBN: (纸本)9798400709418

Clinical picture classification, pattern recognition, and quantification have seen significant advancements with the help of artificial intelligence, particularly through deep learning techniques. Deep learning has rapidly emerged as the most rapidly evolving field within AI, and its applications have been successfully demonstrated across various domains, including medicine. this review briefly examines recent applied research in several medical fields, such as neurology, brain imaging, retinal analysis, pneumonias, computerized pathology, breast imaging, cardiovascular studies, musculoskeletal imaging, and gastrointestinal imaging. Deep learning networks prove to be highly effective when dealing with large scale medical datasets, enabling information discovery, knowledge dissemination, and knowledge-based prediction. this research aims to present both foundational knowledge and state-of-the-art deep learning techniques to facilitate the interpretation and analysis of medical images. the primary objectives of this work are to explore advancements in medical image processing research and implement the identified and addressed key criteria in practical applications.

关键词： KEYWORDS

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Hyperspectral Anomaly Detection based on Autoencoder using Superpixel Manifold Constraint 22

Hyperspectral Anomaly Detection based on Autoencoder using S...

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Proceedings of the 2022 5th international Conference on Artificial Intelligence and pattern recognition

作者： Yuquan Gan Wenqiang Li Ying Liu Jinglu He Ji Zhang School of Telecommunication and Information Engineering Center for Image and Information ProcessingXi'an University of Posts and Telecommunications China University of Southern Queensland Australia

ISBN: (纸本)9781450396899

In the field of hyperspectral anomaly detection, autoencoder (AE) have become a hot research topic due to their unsupervised characteristics and powerful feature extraction capability. However, autoencoders do not keep the spatial structure information of the original data well during the training process, and is affected by anomalies, resulting in poor detection performance. To address these problems, a hyperspectral anomaly detection method based on autoencoders with superpixel manifold constraints is proposed. Firstly, superpixel segmentation technique is used to obtain the superpixels of the hyperspectral image, and then the manifold learning method is used to learn the embedded manifold that based on the superpixels. Secondly, the learned manifold constraints are embedded in the autoencoder to learn the potential representation, which can maintain the consistency of the local spatial and geometric structure of the hyperspectral images (HSI). Finally, anomalies are detected by computing reconstruction errors of the autoencoder. Extensive experiments are conducted on three datasets, and the experimental results show that the proposed method has better detection performance than other hyperspectral anomaly detectors.

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