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A Novel Method for Near-Duplicate image Detection Using Global Features 1

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Proceedings of the 7th International conference on Advances in Computing and Data Sciences, ICACDS 2023

作者： Meena, Kunj Bihari Tyagi, Vipin Jaypee University of Engineering and Technology Guna – MP Raghogarh India

ISBN: (数字)9783031379406

ISBN: (纸本)9783031379390

The rapid growth of digital multimedia content has led to an increase in near-duplicate images. Near-duplicate image detection is a critical task in the field of multimedia forensics, which aims to detect and identify illegally distributed copies of an original image. Gaussian Hermite Moments (GHM) have been proven to be an effective global feature for image representation and analysis in various computer vision tasks, including image forgery detection. In this paper, we propose a novel near-duplicate image detection method that utilizes GHM as the global feature descriptor. We conducted experiments on the CoMoFoD image dataset. The experimental results demonstrate that the proposed method outperforms existing methods for near-duplicate image detection under various post-processing operations and geometric transformations, particularly scaling and rotation. Moreover, the proposed method is significantly faster than existing methods for near-duplicate image detection and it can potentially make the proposed method more practical for real-world applications. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Digital forensics

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InVideo Search: Scene Description Clustering and Integrating image and Audio Captioning for Enhanced Video Search 21st

InVideo Search: Scene Description Clustering and Integrating...

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21st International conference on distributed Computing and Intelligent Technology

作者： Khan, Almira Asif Muhammed Shaji, Asher Mathews Sujith, Devika Nath, Aneesh G. Udmale, Sandeep S. TKM Coll Engn Dept Comp Sci & Engn Kollam Kerala India Veermata Jijabai Technol Inst VJTI Dept Comp Engn & Informat Technol Mumbai 400019 Maharashtra India

ISBN: (纸本)9783031814037;9783031814044

In the digital era, the vast amount of long videos highlights the need for an advanced system to efficiently find specific segments based on text descriptions. The proposed solution aims to develop a video content retrieval system by utilizing established tools in video analysis, image/video captioning, and information retrieval. The work is organized into three modules: Keyframe extraction, Captioning, and Query-based searching. Our primary focus is on effectively identifying key elements within these videos, achieving a semantic understanding of these videos in the form of captions, and establishing precise matches of user queries. In short, users input descriptive text prompts, and the application, equipped with pre-trained models, promptly and accurately identifies and retrieves the video segments. This application carries significant potential across various domains, from enabling efficient scene location in movies to helping extract knowledge from educational lectures. Furthermore, this research fundamentally departs from conventional video search methods that heavily rely on metadata or manually assigned tags, which often fall short of capturing the nuances of video content. This work also focuses on a multimodal captioning strategy to integrate more semantic content. Clustering of captions was also incorporated to improve search performance in the case of long videos. This approach allows us to deliver a robust and efficient solution, taking advantage of the extensive knowledge and research underlying these established technologies. Beyond the immediate applications, this research has broader applications in fields like natural language understanding, multimedia analysis, and information retrieval.

关键词： Clustering Keyframe extraction Natural language processing Video content retrieval system

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Benchmarking Federated Learning on High-Performance Computing: Aggregation methods and Their Impact

Benchmarking Federated Learning on High-Performance Computin...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Daniela Annunziata Marzia Canzaniello Martina Savoia Salvatore Cuomo Francesco Piccialli Department of Mathematics and Applications “R. Caccioppoli” University of Naples Federico II Naples Italy

Federated Learning (FL) diverges from traditional Machine Learning (ML) models decentralizing data utilization, addressing privacy concerns. This approach involves iterative model updates, where individual devices compute gradients based on local data, share updates with a central server, and receive an improved global model. High-Performance Computing (HPC) systems enhance FL efficiency by leveraging parallel processing. In this study, we aim to explore FL efficiency using four aggregation methods on three datasets across six clients, assess metrics like global model accuracy and communication efficiency, and evaluate FL on HPC. We employ Flower, a versatile FL framework, in our experiments. Our chosen datasets include MNIST, Digits, and Semeion Handwritten Digit, distributed among two clients each. We utilize NVIDIA GPUs for computation, with aggregation methods such as FedAvg, FedProx, FedOpt, and FedYogi. Metrics include Convergence Time, Global Model Accuracy, Communication Efficiency, and HPC Throughput. The results will provide insights into FL performance, especially in HPC environments, impacting convergence, communication, and resource utilization.

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ESIREOS: Efficient, Scalable, Internal, Relative Evaluation of Outliers Solutions 29

ESIREOS: Efficient, Scalable, Internal, Relative Evaluation ...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Alves, William A. Marques, Henrique O. Naldi, Murilo C. Sander, Jorg Universidade Federal de São Carlos Department of Computing Science Sorocaba Brazil University of Southern Denmark Department of Mathematics and Computer Science Odense Denmark Universidade Federal de São Carlos Department of Computing Science São Carlos Brazil University of Alberta Department of Computing Science Edmonton Canada

ISBN: (纸本)9798350330717

Anomaly (outlier) detection is one of the main tasks of data mining. Since anomalies can translate into important information in numerous fields, several methods have been developed to identify them. Unsupervised methods for outlier detection, which is the focus of this work, have become increasingly important due to the lack of labeled data in many applications. A common challenge when dealing with unsupervised methods, however, is how to evaluate the quality of their results. Without labels available, one has to rely on the so-called internal evaluation, which is based solely on the data and the assessed solutions. In this context, IREOS was proposed as the first internal evaluation measure for unsupervised anomaly detection. IREOS allows one to select better solutions (algorithms, parameters) for a given problem using only intrinsic information from the data. One major limitation of IREOS, however, is the demand to train many highly complex classifiers, which makes it impractical for large datasets. In this work, we propose the first Efficient, Scalable version of IREOS, ESIREOS. We address the computational performance shortcomings of IREOS by using Massive parallel Computing (MPC) techniques that efficiently implement horizontal computational scaling for many machine learning problems. ESIREOS also makes use of approximated nearest neighbor graphs (NNGs) to reduce the volume of data and processing power demanded by IREOS without any significant loss in the quality of the results. We evaluate ESIREOS theoretically by estimating its asymptotic complexity and empirically with experiments on real and synthetic datasets to assess its effectiveness and efficiency compared to the original version. Our results showed that ESIREOS significantly improved the computational runtime compared to the original IREOS while maintaining quality. Also, ESIREOS proved capable of evaluating solutions for very large datasets, even those which IREOS cannot evaluate in a feasible time.

关键词： Data Mining Massive parallel Computing Outlier Detection Unsupervised Evaluation

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A Normalized Mutual Information-based Multi-Modal Medical image Fusion Technique using Non Sub-sampled Shearlet Transform and Method Noise

A Normalized Mutual Information-based Multi-Modal Medical Im...

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International conference on parallel distributed and Grid Computing (PDGC)

作者： Shailesh Bhosekar Prabhishek Singh Deepak Garg School of Computer Science and Artificial Intelligence SR University Warangal Telangana India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

In image processing, image fusion is the process of merging characteristics and information from multiple images to build a single, more complete representation. This approach includes multi-modal medical image fusion, which involves fusing medical images from several imaging modalities such as computed tomography (CT) scans, Positron Emission Tomography (PET) and magnetic resonance imaging (MRI). This integration improves diagnostic accuracy by using each modality's own strengths, resulting in clearer visualization of anatomical structures and clinical situations. This work utilizes CT, PET, and MRI images as modalities for the experimentation purpose. This study (M3IF-NSST-MI) aims to construct a multi modal medical image approach that combines Non Sub-sampled Shearlet Transform (NSST) and method noise to the information content in the fused image, where high pass subbands are fused using Normalized Mutual Information (NMI) based fusion strategy. NSST domain is used for its Multi-Scale Representation and shift invariance. The concept of method noise as post-processing operation that extracts the information which was missed in the initial phase. The wavelet coefficients are fused using NMI-based fusion method. This combination of NSST with NMI-based fusion and method noise provides superior results than many conventional methods. This improves overall visual and parametric quality of fusion results than many compared methods.

关键词： Visualization Accuracy Magnetic resonance imaging Computed tomography Noise Positron emission tomography Medical diagnostic imaging image fusion Mutual information Wavelet coefficients

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An Improved Tracking Method for distributed Passive Sonar System 7

An Improved Tracking Method for Distributed Passive Sonar Sy...

引用

7th International conference on Signal and image processing, ICSIP 2022

作者： Shao, Pengfei Li, Qing Zhou, Bin Hangzhou Applied Acoustics Research Institute Science and Technology on Sonar Laboratory Hangzhou China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

Underwater target tracking by distributed passive sonar system can be regarded as a time-sequence target state parameter estimation problem with multiple observation equations. Currently, the commonly used Bayesian tracking methods mainly include Kalman filter tracking and particle filter tracking methods. However, When the target is maneuvering or located in the fuzzy region of array direction finding, the performance of traditional tracker will decrease sharply and even become invalid. Aiming at the target tracking problem of passive distributed sonar observation system under the above situation, a Bayesian tracking method with radial velocity constraint is described in this paper, and an improved tracking algorithm is proposed, which is verified by simulation and SWELLEx-96 experiment data. © 2022 IEEE.

关键词： Target tracking

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Deep Contrastive Patch-Based Subspace Learning for Camera image Signal processing

Deep Contrastive Patch-Based Subspace Learning for Camera Im...

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2023 IEEE World conference on Applied Intelligence and Computing, AIC 2023

作者： Yang, Yunhao Wang, Yi Bajaj, Chandrajit University of Texas at Austin Austin United States

ISBN: (纸本)9798350310061

Camera image Signal processing(ISP) pipelines can get appealing results in different image signal processing tasks. Nonetheless, the majority of these methods, including those employing an encoder-decoder deep architecture for the task, typically utilize a uniform filter applied consistently across the entire image. However, it is natural to view a camera image as heterogeneous, as the color intensity and the artificial noise are distributed vastly differently, even across the two-dimensional domain of a single image. Varied Moire ringing, motion blur, color-bleaching, or lens-based projection distortions can all potentially lead to a heterogeneous image artifact filtering problem. In this paper, we present a specific patch-based, local subspace deep neural network that improves Camera ISP to be robust to heterogeneous artifacts (especially image denoising). We call our three-fold deep-trained model the Patch Subspace Learning Autoencoder (PSL-AE)11Oursource code is publiclyavailable at https://***/CVC-Lab/Patch-Subspace-Learning-Autoencodd.. The PSL-AE model does not make assumptions regarding uniform levels of image distortion. Instead, it first encodes patches extracted from noisy a nd clean image pairs, with different artifact types or distortion levels, by contrastive learning. Then, the patches of each image are encoded into corresponding soft clusters within their suitable latent sub-space, utilizing a prior mixture model. Furthermore, the decoders undergo training in an unsupervised manner, specifically trained for the image patches present in each cluster. The experiments highlight the adaptability and efficacy through enhanced heterogeneous filtering, both from synthesized artifacts but also realistic SIDD image pairs. © 2023 IEEE.

关键词： image denoising

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A parallel Dual-Task Learning Network for InSAR Phase Retrieval

A Parallel Dual-Task Learning Network for InSAR Phase Retrie...

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IEEE Radar conference (RadarConf)

作者： Zhan, Xu Zhang, Xiaoling Ma, Xiangdong Shi, Jun Zheng, Shunxin Chen, Jiaping Wei, Shunjun Zeng, Tianjiao Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9781665436694

This work focuses on the problem of InSAR phase retrieval. Current methods consist of two cascaded tasks: phase filtering and phase unwrapping. Unavoidable accumulated errors cause precision loss, and serial computations cause efficiency loss. We propose a parallel dual-task learning work to address these issues for high-quality and efficient InSAR phase retrieval. Methodologically, we retrieve the InSAR phase in a parallel manner instead of a serially cascaded one. Specifically, three core phases throughout the whole processing chain are considered, including feature attraction, task learning, and task balance. First, for feature attraction, considering the InSAR image characteristics, we propose a hybrid Trans-Encoder module to attract features locally and nonlocally. Second, regarding the dual-task needs for feature learning, we propose a dual-decoder to denoise and unwrap parallelly. Third, considering the dualtask's different attributes for task balance, we propose an uncertainty-weighted loss to make balances between tasks. Experiments on both simulated and measured data verify the proposed method's higher precision and efficiency compared to other methods. An ability study is conducted that confirms the effectiveness of the proposed modules.

关键词： phase filtering phase unwrapping parallel learning InSAR

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A Comparative Study of Transfer Learning Approaches For Medical image Analysis

A Comparative Study of Transfer Learning Approaches For Medi...

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International conference on parallel distributed and Grid Computing (PDGC)

作者： Prince Priya Malla Sudhakar Sahu School of Electronics Engineering Kalinga Institute of Industrial Technology Bhubaneswar Odisha India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Deep learning based approaches have been extensively used for the image classification task with improved accuracy and lower computational complexity. The deep learning approaches have shown outstanding performances when a huge amount of datasets are available to train the model. However, in medical image analysis, sufficient data is not available to train deep learning models. To overcome these issues, a new approach called transfer learning has been introduced for the medical image classification task. This approach has shown outstanding performances with less computational complexity. In the case of transfer learning approaches, the knowledge acquired by training the model with a non-medical image dataset can be transferred to the target dataset. This paper presents a comparative study on the application of transfer learning techniques for the medical image analysis task. This paper demonstrated a complete analysis of the architecture and performance of numerous pre-trained models on the imageNet dataset. Further, This paper presents the significance of the transfer learning techniques for medical image processing tasks for example brain tumor detection, pneumonia classification, and COVID-19 detection by analyzing various state-of-the-art methods. The experimental results indicate that the performance of transfer learning-based methods can be improved by fine-tuning these models using medical-specific datasets.

关键词： Deep learning Analytical models Accuracy Computational modeling Transfer learning Brain modeling Medical diagnosis Computational complexity Medical diagnostic imaging image classification

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Scalable SubXPCA on a distributed Platform Applied to Classification of Hyperspectral images

Scalable SubXPCA on a Distributed Platform Applied to Classi...

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conference on Information and Communication Technology (CICT)

作者： Bogolu Rupa Atul Negi School of Computer and Information Sciences University of Hyderabad Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad Telangana India

ISBN: (数字)9798331531539

ISBN: (纸本)9798331531546

The exponential growth of technological advancements in satellite and airborne remote sensing is giving rise to large volumes of high-dimensional hyperspectral image data. Apache Spark is one of the most popular, extensively used and open-source distributed processing frameworks, which is proven effective in processing large volumes of remotely sensed hyperspectral images in a time-efficient manner. Open-source distributed processing frameworks have proven effective in processing large volumes of remotely sensed hyperspectral images quickly and efficiently. While computational power has been increasing, the rate of data accumulation is more than the processing capabilities. Therefore, more efficient algorithms such as dimensionality reduction are needed to process and get accurate performance for the application. This paper proposes an efficient and parallel spectral dimensionality reduction approach based on feature partitioning principal component analysis called scalable SubXPCA. We implemented scalable SubXPCA on a spark cluster distributed environment. We compared scalable SubXPCA against other distributed feature partitioning and various non-feature partitioning dimensionality reduction methods. Our experiments on different real and synthetic datasets of hyperspectral images confirm that SubXPCA classification performance is not only better than its competitors but also that the running time of SubXPCA is better in distributed processing than serial processing. As the size of the hyperspectral image dataset increased, SubXPCA showed a speed up factor of 5.7× and more in the spark cluster compared to the serial version.

关键词： Dimensionality reduction distributed processing Accuracy Satellites Partitioning algorithms Information and communication technology Sparks Hyperspectral imaging Principal component analysis Synthetic data

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