2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP)最新文献_第5页

Improving Real-Time Pedestrian Detection Using Adaptive Confidence Thresholding and Inter-Frame Correlation 利用自适应置信度阈值和帧间相关性改进实时行人检测

2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2018-08-01 DOI: 10.1109/MMSP.2018.8547103

M. Al-Shatnawi, Vida Movahedi, A. Asif, Aijun An

引用次数: 1

Deep Transfer Learning for Hyperspectral Image Classification 高光谱图像分类的深度迁移学习

2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2018-08-01 DOI: 10.1109/MMSP.2018.8547139

Jianzhe Lin, R. Ward, Z. J. Wang

{"title":"Deep Transfer Learning for Hyperspectral Image Classification","authors":"Jianzhe Lin, R. Ward, Z. J. Wang","doi":"10.1109/MMSP.2018.8547139","DOIUrl":"https://doi.org/10.1109/MMSP.2018.8547139","url":null,"abstract":"Hyperspectral image (HSI) includes a vast quantities of samples, large number of bands, as well as randomly occurring redundancy. Classifying such complex data is challenging, and the classification performance generally is affected significantly by the amount of labeled training samples. Collecting such labeled training samples is labor and time consuming, motivating the idea of borrowing and reusing labeled samples from other preexisting related images. Therefore transfer learning, which can mitigate the semantic gap between existing and new HSI, has recently drawn increasing research attention. However, existing transfer learning methods for HSI which concentrated on how to overcome the divergence among images, may neglect the high level latent features during the transfer learning process. In this paper, we present two novel ideas based on this observation. We propose constructing and connecting higher level features for the source and target HSI data, to further overcome the cross-domain disparity. Different from existing methods, no priori knowledge on the target domain is needed for the proposed classification framework, and the proposed framework works for both homogeneous and heterogenous HSI data. Experimental results on real world hyperspectral images indicate the significance of the proposed method in HSI classification.","PeriodicalId":137522,"journal":{"name":"2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123020165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 19

Quality Assessment of Deep-Learning-Based Image Compression 基于深度学习的图像压缩质量评估

2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2018-08-01 DOI: 10.1109/MMSP.2018.8547064

G. Valenzise, Andrei I. Purica, Vedad Hulusic, Marco Cagnazzo

{"title":"Quality Assessment of Deep-Learning-Based Image Compression","authors":"G. Valenzise, Andrei I. Purica, Vedad Hulusic, Marco Cagnazzo","doi":"10.1109/MMSP.2018.8547064","DOIUrl":"https://doi.org/10.1109/MMSP.2018.8547064","url":null,"abstract":"Image compression standards rely on predictive coding, transform coding, quantization and entropy coding, in order to achieve high compression performance. Very recently, deep generative models have been used to optimize or replace some of these operations, with very promising results. However, so far no systematic and independent study of the coding performance of these algorithms has been carried out. In this paper, for the first time, we conduct a subjective evaluation of two recent deep-learning-based image compression algorithms, comparing them to JPEG 2000 and to the recent BPG image codec based on HEVC Intra. We found that compression approaches based on deep auto-encoders can achieve coding performance higher than JPEG 2000, and sometimes as good as BPG. We also show experimentally that the PSNR metric is to be avoided when evaluating the visual quality of deep-learning-based methods, as their artifacts have different characteristics from those of DCT or wavelet-based codecs. In particular, images compressed at low bitrate appear more natural than JPEG 2000 coded pictures, according to a no-reference naturalness measure. Our study indicates that deep generative models are likely to bring huge innovation into the video coding arena in the coming years.","PeriodicalId":137522,"journal":{"name":"2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121655117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 24

Motion Compensated Prediction for Translational Camera Motion in Spherical Video Coding 球面视频编码中平移摄像机运动的运动补偿预测

2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2018-08-01 DOI: 10.1109/MMSP.2018.8547066

B. Vishwanath, Tejaswi Nanjundaswamy, K. Rose

{"title":"Motion Compensated Prediction for Translational Camera Motion in Spherical Video Coding","authors":"B. Vishwanath, Tejaswi Nanjundaswamy, K. Rose","doi":"10.1109/MMSP.2018.8547066","DOIUrl":"https://doi.org/10.1109/MMSP.2018.8547066","url":null,"abstract":"Spherical video is the key driving factor for the growth of virtual reality and augmented reality applications, as it offers truly immersive experience by capturing the entire 3D surroundings. However, it represents an enormous amount of data for storage/transmission and success of all related applications is critically dependent on efficient compression. A frequently encountered type of content in this video format is due to translational motion of the camera (e.g., a camera mounted on a moving vehicle). Existing approaches simply project this video onto a plane and use block based translational motion model for capturing the motion of the objects between the frames. This ad-hoc simplified approach completely ignores the complex deformities of objects caused due to the combined effect of the moving camera and projection onto a plane, rendering it significantly suboptimal. In this paper, we provide an efficient solution tailored to this problem. Specifically, we propose to perform motion compensated prediction by translating pixels along their geodesics, which intersect at the poles corresponding to the camera velocity vector. This setup not only captures the surrounding objects' motion exactly along the geodesics of the sphere, but also accurately accounts for the deformations caused due to projection on the sphere. Experimental results demonstrate that the proposed framework achieves very significant gains over existing motion models.","PeriodicalId":137522,"journal":{"name":"2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123860373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Video Classification of Farming Activities with Motion-Adaptive Feature Sampling 基于运动自适应特征采样的农业视频分类

2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2018-08-01 DOI: 10.1109/MMSP.2018.8547117

He Liu, A. Reibman, A. Ault, J. Krogmeier

引用次数: 2

Identifying Image Provenance: An Analysis of Mobile Instant Messaging Apps 识别图像来源:对移动即时通讯应用的分析

2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2018-08-01 DOI: 10.1109/MMSP.2018.8547050

Quoc-Tin Phan, Cecilia Pasquini, G. Boato, F. D. Natale

{"title":"Identifying Image Provenance: An Analysis of Mobile Instant Messaging Apps","authors":"Quoc-Tin Phan, Cecilia Pasquini, G. Boato, F. D. Natale","doi":"10.1109/MMSP.2018.8547050","DOIUrl":"https://doi.org/10.1109/MMSP.2018.8547050","url":null,"abstract":"Studying the impact of sharing platforms like social networks and messaging services on multimedia content nowadays represents a due step in multimedia forensics research. In this framework, we study the characteristics of images that are uploaded and shared through three popular mobile messaging apps combined with two different sending mobile operating systems (OS). In our analysis, we consider information contained both in the image signal and in the metadata of the image file. We show that it is generally possible to identify a posteriori the last app and the OS that have been used for uploading. This is done by considering different scenarios involving images shared both once and twice. Moreover, we show that, by leveraging the knowledge of the last sharing app and system, it is possible to retrieve information on the previous sharing step for double shared images. In relation to prior works, a discussion on the influence of the rescaling and recompression mechanism - usually performed differently through apps and OSs - is also proposed, and the feasibility of retrieving the compression parameters of the image before being shared is assessed.","PeriodicalId":137522,"journal":{"name":"2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126219759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

Sparse Hartley Modeling for Fast Image Extrapolation 快速图像外推的稀疏Hartley建模

2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2018-08-01 DOI: 10.1109/MMSP.2018.8547100

Nils Genser, Simon Grosche, Jürgen Seiler, André Kaup

引用次数: 1

Deep Siamese Network for Multiple Object Tracking 用于多目标跟踪的深度连体网络

2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2018-08-01 DOI: 10.1109/MMSP.2018.8547137

Bonan Cuan, Khalid Idrissi, Christophe Garcia

引用次数: 10

Color Noise-Based Feature for Splicing Detection and Localization 基于颜色噪声的拼接检测与定位方法

2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2018-08-01 DOI: 10.1109/MMSP.2018.8547093

C. Destruel, V. Itier, O. Strauss, W. Puech

{"title":"Color Noise-Based Feature for Splicing Detection and Localization","authors":"C. Destruel, V. Itier, O. Strauss, W. Puech","doi":"10.1109/MMSP.2018.8547093","DOIUrl":"https://doi.org/10.1109/MMSP.2018.8547093","url":null,"abstract":"Images that have been altered and more specifically spliced together have invaded the digital domain due to the ease with which we are able to copy and paste them. To detect such forgeries the digital image processing community is proposing new automatic algorithms designed to help human operators reveal manipulated images. In this paper, we focus on a local detection system, which considers which tampered areas produce local statistical effects that do not impact neighboring areas or the image as a whole. We propose to study how the definition of local blocks, considering their size and overlap, impacts final pixel detection. We also propose new features which are an original way to consider the noise of an image as a colored signal. Indeed, in a non-forged image, there is a high correlation of noise between the three color channels R, G and B. We show that an optimal configuration can be defined and in this case the proposed approach outperforms several previously proposed methods using the same tested dataset, in uncompressed and JPEG modes. Note, in this paper we only focus on feature extraction without using machine learning.","PeriodicalId":137522,"journal":{"name":"2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133972866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Spatial Reinforcement and Immersive Audio 空间强化和沉浸式音频

2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2018-08-01 DOI: 10.1109/MMSP.2018.8547099

Timothy Bartoo, R. Whittaker, Dave Haydon

引用次数: 0