Journal of Visual Communication and Image Representation最新文献_第3页

A GAN-based anti-forensics method by modifying the quantization table in JPEG header file 一种基于gan的反取证方法，通过修改JPEG头文件中的量化表

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-04-23 DOI: 10.1016/j.jvcir.2025.104462

Hao Wang , Xin Cheng , Hao Wu , Xiangyang Luo , Bin Ma , Hui Zong , Jiawei Zhang , Jinwei Wang

{"title":"A GAN-based anti-forensics method by modifying the quantization table in JPEG header file","authors":"Hao Wang , Xin Cheng , Hao Wu , Xiangyang Luo , Bin Ma , Hui Zong , Jiawei Zhang , Jinwei Wang","doi":"10.1016/j.jvcir.2025.104462","DOIUrl":"10.1016/j.jvcir.2025.104462","url":null,"abstract":"<div><div>It is crucial to detect double JPEG compression images in digital image forensics. When detecting recompressed images, most detection methods assume that the quantization table in the JPEG header is safe. The method fails once the quantization table in the header file is tampered with. Inspired by this phenomenon, this paper proposes a double JPEG compression anti-detection method based on the generative adversarial network (GAN) by modifying the quantization table of JPEG header files. The proposed method draws on the structure of GAN to modify the quantization table by gradient descent. Also, our proposed method introduces adversarial loss to determine the direction of the modification so that the modified quantization table can be used for cheat detection methods. The proposed method achieves the aim of anti-detection and only needs to replace the original quantization table after the net training. Experiments show that the proposed method has a high anti-detection rate and generates images with high visual quality.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"110 ","pages":"Article 104462"},"PeriodicalIF":2.6,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EPSA-VPR: A lightweight visual place recognition method with an Efficient Patch Saliency-weighted Aggregator EPSA-VPR：一种基于高效斑块显著性加权聚合器的轻量级视觉位置识别方法

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-04-22 DOI: 10.1016/j.jvcir.2025.104440

Jiwei Nie , Qǐxı̄ Zhào , Dingyu Xue , Feng Pan , Wei Liu

{"title":"EPSA-VPR: A lightweight visual place recognition method with an Efficient Patch Saliency-weighted Aggregator","authors":"Jiwei Nie , Qǐxı̄ Zhào , Dingyu Xue , Feng Pan , Wei Liu","doi":"10.1016/j.jvcir.2025.104440","DOIUrl":"10.1016/j.jvcir.2025.104440","url":null,"abstract":"<div><div>Visual Place Recognition (VPR) is important in autonomous driving, as it enables vehicles to identify their positions using a pre-built database. In this domain, prior research highlights the advantages of recognizing and emphasizing high-saliency local features in descriptor aggregation for performance improvement. Following this path, we introduce EPSA-VPR, a lightweight VPR method incorporating a proposed Efficient Patch Saliency-weighted Aggregator (EPSA), additionally addressing the computational efficiency demands of large-scale scenarios. With almost negligible computational requirements, EPSA efficiently calculates and integrates the local saliency into the global descriptor. To quantitatively evaluate the effectiveness, EPSA-VPR is validated across various VPR benchmarks. The comprehensive evaluations confirm that our method outperforms existing advanced VPR technologies and achieves competitive performance. Notably, EPSA-VPR also derives the second-best performance among two-stage VPR methods, without the need for re-ranking computations. Moreover, the effectiveness of our model is sustainable even with considerable dimension reduction. Visualization analysis reveals the interpretability of EPSA-VPR that after training, the backbone network learns to attach more attention on the task-related elements, which makes the final descriptor more discriminative.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"110 ","pages":"Article 104440"},"PeriodicalIF":2.6,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Opinion-unaware blind quality assessment of AI-generated omnidirectional images based on deep feature statistics 基于深度特征统计的人工智能生成全向图像的无意见盲质量评估

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-04-22 DOI: 10.1016/j.jvcir.2025.104461

Xuelin Liu, Jiebin Yan, Yuming Fang, Jingwen Hou

{"title":"Opinion-unaware blind quality assessment of AI-generated omnidirectional images based on deep feature statistics","authors":"Xuelin Liu, Jiebin Yan, Yuming Fang, Jingwen Hou","doi":"10.1016/j.jvcir.2025.104461","DOIUrl":"10.1016/j.jvcir.2025.104461","url":null,"abstract":"<div><div>The advancement of artificial intelligence generated content (AIGC) and virtual reality (VR) technologies have prompted AI-generated omnidirectional images (AGOI) to gradually into people’s daily lives. Compared to natural omnidirectional images, AGOIs exhibit traditional low-level technical distortions and high-level semantic distortions, which can severely affect the immersive experience for users in practical applications. Consequently, there is an urgent need for thorough research and precise evaluation of AGOI quality. In this paper, we propose a novel opinion-unaware (OU) blind quality assessment approach for AGOIs based on deep feature statistics. Specifically, we first transform the AGOIs in equirectangular projection (ERP) format into a set of six cubemap projection (CMP)-converted viewport images, and extract viewport-wise multi-layer deep features from the pre-trained neural network backbone. Based on the deep representations, the multivariate Gaussian (MVG) models are subsequently fitted. The individual quality score for each CMP-converted image is calculated by comparing it against the corresponding fitted pristine MVG model. The final quality score for a testing AGOI is then computed by aggregating these individual quality scores. We conduct comprehensive experiments using the existing AGOIQA database and the experimental results show that the proposed OU-BAGOIQA model outperforms current state-of-the-art OU blind image quality assessment models. The ablation study has also been conducted to validate the effectiveness of our method.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"110 ","pages":"Article 104461"},"PeriodicalIF":2.6,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Attention mechanism based multimodal feature fusion network for human action recognition 基于注意机制的多模态特征融合网络人体动作识别

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-04-21 DOI: 10.1016/j.jvcir.2025.104459

Xu Zhao , Chao Tang , Huosheng Hu , Wenjian Wang , Shuo Qiao , Anyang Tong

{"title":"Attention mechanism based multimodal feature fusion network for human action recognition","authors":"Xu Zhao , Chao Tang , Huosheng Hu , Wenjian Wang , Shuo Qiao , Anyang Tong","doi":"10.1016/j.jvcir.2025.104459","DOIUrl":"10.1016/j.jvcir.2025.104459","url":null,"abstract":"<div><div>Current human action recognition (HAR) methods focus on integrating multiple data modalities, such as skeleton data and RGB data. However, they struggle to exploit motion correlation information in skeleton data and rely on spatial representations from RGB modalities. This paper proposes a novel Attention-based Multimodal Feature Integration Network (AMFI-Net) designed to enhance modal fusion and improve recognition accuracy. First, RGB and skeleton data undergo multi-level preprocessing to obtain differential movement representations, which are then input into a heterogeneous network for separate multimodal feature extraction. Next, an adaptive fusion strategy is employed to enhance the integration of these multimodal features. Finally, the network assesses the confidence level of weighted skeleton information to determine the extent and type of appearance information to be used in the final feature integration. Experiments conducted on the NTU-RGB + D dataset demonstrate that the proposed method is feasible, leading to significant improvements in human action recognition accuracy.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"110 ","pages":"Article 104459"},"PeriodicalIF":2.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Seg-Cam: Enhancing interpretability analysis in segmentation networks Seg-Cam：增强分割网络的可解释性分析

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-04-21 DOI: 10.1016/j.jvcir.2025.104467

Weihua Wu , Chunming Ye , Xufei Liao

{"title":"Seg-Cam: Enhancing interpretability analysis in segmentation networks","authors":"Weihua Wu , Chunming Ye , Xufei Liao","doi":"10.1016/j.jvcir.2025.104467","DOIUrl":"10.1016/j.jvcir.2025.104467","url":null,"abstract":"<div><div>Existing interpretability analysis methods face significant limitations when applied to segmentation networks, such as limited applicability and unclear visualization of weight distribution. To address these issues, a novel approach for calculating network layer weights was established for segmentation networks, such as encoder-decoder networks. Rather than processing individual parameters, this method computes gradients based on pixel-level information. It improves the weight calculation model in the Grad-Cam method by removing the constraint that the model’s output layer must be a one-dimensional vector. This modification extends its applicability beyond traditional CNN classification models to include those that generate feature maps as output, such as segmentation models. It also improves the visualization process by calculating the distribution of feature map weights for the specified layer without changing the model architecture or retraining. Utilizing the image segmentation task as the project context, the seg-cam visualization scheme is incorporated into the initial model. This scheme enables the visualization of parameter weights for each network layer, facilitating post-training analysis and model calibration. This approach enhances the interpretability of segmentation networks, particularly in cases where the head layer contains many parameters, making interpretation challenging.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"110 ","pages":"Article 104467"},"PeriodicalIF":2.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hard-UNet architecture for medical image segmentation using position encoding generator: LSA based encoder 基于位置编码生成器的医学图像分割硬网结构：基于LSA的编码器

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-04-17 DOI: 10.1016/j.jvcir.2025.104452

Chia-Jui Chen

引用次数: 0

Learning disruptor-suppressed response variation-aware multi-regularized correlation filter for visual tracking 用于视觉跟踪的学习干扰抑制响应变差感知多正则化相关滤波器

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-04-17 DOI: 10.1016/j.jvcir.2025.104458

Sathishkumar Moorthy , Sachin Sakthi K.S. , Sathiyamoorthi Arthanari , Jae Hoon Jeong , Young Hoon Joo

{"title":"Learning disruptor-suppressed response variation-aware multi-regularized correlation filter for visual tracking","authors":"Sathishkumar Moorthy , Sachin Sakthi K.S. , Sathiyamoorthi Arthanari , Jae Hoon Jeong , Young Hoon Joo","doi":"10.1016/j.jvcir.2025.104458","DOIUrl":"10.1016/j.jvcir.2025.104458","url":null,"abstract":"<div><div>Discriminative correlation filters (DCF) are widely used in object tracking for their high accuracy and computational efficiency. However, conventional DCF methods, which rely only on consecutive frames, often lack robustness due to limited temporal information and can suffer from noise introduced by historical frames. To address these limitations, we propose a novel disruptor-suppressed response variation-aware multi-regularized tracking (DSRVMRT) method. This approach improves tracking stability by incorporating historical interval information in filter training, thus leveraging a broader temporal context. Our method includes response deviation regularization to maintain consistent response quality and introduces a receptive channel weight distribution to enhance channel reliability. Additionally, we implement a disruptor-aware scheme using response bucketing, which detects and penalizes areas affected by similar objects or partial occlusions, reducing tracking disruptions. Extensive evaluations on public tracking benchmarks demonstrate that DSRVMRT achieves superior accuracy, robustness, and effectiveness compared to existing methods.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"110 ","pages":"Article 104458"},"PeriodicalIF":2.6,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic kernel-based adaptive spatial aggregation for learned image compression 基于动态核的自适应空间聚合学习图像压缩

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-04-15 DOI: 10.1016/j.jvcir.2025.104456

Huairui Wang , Nianxiang Fu , Zhenzhong Chen , Shan Liu

{"title":"Dynamic kernel-based adaptive spatial aggregation for learned image compression","authors":"Huairui Wang , Nianxiang Fu , Zhenzhong Chen , Shan Liu","doi":"10.1016/j.jvcir.2025.104456","DOIUrl":"10.1016/j.jvcir.2025.104456","url":null,"abstract":"<div><div>Learned image compression methods have shown remarkable performance and expansion potential compared to traditional codecs. Currently, there are two mainstream image compression frameworks: one uses stacked convolution and other uses window-based self-attention for transform coding, most of which aggregate valuable dependencies in a fixed spatial range. In this paper, we focus on extending content-adaptive aggregation capability and propose a dynamic kernel-based transform coding. The proposed adaptive aggregation generates kernel offsets to capture valuable information with dynamic sampling convolution to help transform. With the adaptive aggregation strategy and the sharing weights mechanism, our method can achieve promising transform capability with acceptable model complexity. Besides, considering the coarse hyper prior, the channel-wise, and the spatial context, we formulate a generalized entropy model. Based on it, we introduce dynamic kernel in hyper-prior to generate more expressive side information context. Furthermore, we propose an asymmetric sparse entropy model according to the investigation of the spatial and variance characteristics of the grouped latents. The proposed entropy model can facilitate entropy coding to reduce statistical redundancy while maintaining inference efficiency. Experimental results demonstrate that our method achieves superior rate–distortion performance on three benchmarks compared to the state-of-the-art learning-based methods.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"110 ","pages":"Article 104456"},"PeriodicalIF":2.6,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rotation translation matrices analysis method for action recognition of construction equipment 施工设备动作识别的旋转平移矩阵分析方法

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-04-11 DOI: 10.1016/j.jvcir.2025.104460

Ziwei Liu, Jiazhong Yu, Rundong Cao, Qinghua Liang, Shui Liu, Linsu Shi

{"title":"Rotation translation matrices analysis method for action recognition of construction equipment","authors":"Ziwei Liu, Jiazhong Yu, Rundong Cao, Qinghua Liang, Shui Liu, Linsu Shi","doi":"10.1016/j.jvcir.2025.104460","DOIUrl":"10.1016/j.jvcir.2025.104460","url":null,"abstract":"<div><div>Lack of intelligence is one of the primary factors hindering the implementation of video surveillance. Action recognition is a method used to enhance the effectiveness of video surveillance and has garnered the interest of numerous researchers. In recent years, the advancement of deep learning (DL) frameworks has led to the proposal of numerous DL-based action recognition models. However, most of these models exhibit poor performance in recognizing actions of construction equipment, primarily due to the presence of multiple targets in complex real-life scenes. Considering the above information, we have developed a method for action recognition that involves analyzing the motion of the fundamental components of construction vehicles. Firstly, we estimate the essential components of construction vehicles from the video inputs using an instance segmentation method. Secondly, to assess the motion state of the robotic arm of the equipment, we have developed an analysis method based on rotation and translation (RT) matrices. We propose to examine the relationship between action recognition of construction vehicles and RT matrices. The evaluations of the respective datasets were conducted. The experimental results validate the effectiveness of the proposed framework, and our model demonstrates state-of-the-art performance in action recognition of construction equipment. We utilize RT matrices to model the degrees of movement of construction equipment, allowing us to analyze their actions and providing a unique perspective on action recognition. We believe that the proposed framework can facilitate the transition of video surveillance techniques from research to practical applications, ultimately generating economic value.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"110 ","pages":"Article 104460"},"PeriodicalIF":2.6,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Contrastive attention and fine-grained feature fusion for artistic style transfer 对比关注与细纹特征融合，实现艺术风格的传递

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2025-04-11 DOI: 10.1016/j.jvcir.2025.104451

Honggang Zhao , Beinan Zhang , Yi-Jun Yang

{"title":"Contrastive attention and fine-grained feature fusion for artistic style transfer","authors":"Honggang Zhao , Beinan Zhang , Yi-Jun Yang","doi":"10.1016/j.jvcir.2025.104451","DOIUrl":"10.1016/j.jvcir.2025.104451","url":null,"abstract":"<div><div>In contemporary image processing, creative image alteration plays a crucial role. Recent studies on style transfer have utilized attention mechanisms to capture the aesthetic and artistic expressions of style images. This method converts style images into tokens by initially assessing attention levels and subsequently employing a decoder to transfer the artistic style of the image. However, this approach often discards many fine-grained style elements due to the low semantic similarity between the original and style images. This may result in discordant or conspicuous artifacts. We propose MccSTN, an innovative framework for style representation and transfer, designed to adapt to contemporary arbitrary image style transfers as a solution to this problem. Specifically, we introduce the Mccformer feature fusion module, which integrates fine-grained features from content images with aesthetic characteristics from style images. Mccformer is utilized to generate feature maps. The target image is then produced by inputting the feature map into the decoder. We consider the relationship between individual styles and the overall style distribution to streamline the model and enhance training efficiency. We present a multi-scale augmented contrast module that leverages a substantial number of image pairs to learn style representations. Code will be posted on <span><span>https://github.com/haizhu12/MccSTN</span><svg><path></path></svg></span></div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"110 ","pages":"Article 104451"},"PeriodicalIF":2.6,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0