2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)最新文献_第8页

Video content and structure description based on keyframes, clusters and storyboards 基于关键帧、集群和故事板的视频内容和结构描述

2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-10-01 DOI: 10.1109/MMSP.2015.7340866

Marc Junyent, Pablo Beltrán, Miquel A. Farre, J. Pont-Tuset, Alexandre Chapiro, A. Smolic

引用次数: 2

Soft binary segmentation-based backlit image enhancement 基于软二值分割的背光图像增强

2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-10-01 DOI: 10.1109/MMSP.2015.7340808

Zhenhao Li, K. Cheng, Xiaolin Wu

引用次数: 10

Hybrid hidden Markov model for mixed continuous/continuous and discrete/continuous data modeling 混合连续/连续和离散/连续数据建模的混合隐马尔可夫模型

2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-10-01 DOI: 10.1109/MMSP.2015.7340853

E. Epaillard, N. Bouguila

{"title":"Hybrid hidden Markov model for mixed continuous/continuous and discrete/continuous data modeling","authors":"E. Epaillard, N. Bouguila","doi":"10.1109/MMSP.2015.7340853","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340853","url":null,"abstract":"We propose a hybrid hidden Markov model (HMM) for mixed continuous/continuous and discrete/continuous outcomes. The Expectation-Maximization (EM) procedure used for the parameters estimation relies on a combination of local and global quantities that relate to outcomes of a specific type and to all outcomes, respectively. The new model is implemented for discrete outcomes and continuous outcomes that follow Gaussian, Dirichlet, and Gamma distributions. Experiments led with synthetic data show same or better modeling accuracy compared to a fully Gaussian HMM, with less parameters and thus a shorter convergence time for high dimensional data. Finally, the approach is validated with real data in a change detection scenario between a pair of images, one of which has been captured by an optic sensor and the other by a synthesis aperture radar (SAR) sensor. Based on the properties of the noises corrupting these images, a hybrid Gamma-Gaussian HMM is trained and the likelihood of the data with respect to the model is used to detect changes. The obtained results are in line with the most recent approaches for this task with the advantage of providing a very compact representation of the data.","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125150618","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: 5

Motion blur modelling for hierarchically anchored motion with discontinuities 运动模糊建模的层次锚定运动与不连续

2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-10-01 DOI: 10.1109/MMSP.2015.7340817

Dominic Rüfenacht, R. Mathew, D. Taubman

引用次数: 1

Improved downstream rate-distortion performance of SHVC in DASH using sub-layer-selective interlayer prediction 利用子层选择性层间预测改善DASH中SHVC的下游速率失真性能

2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-10-01 DOI: 10.1109/MMSP.2015.7340848

Xuehui Huang, M. Hannuksela, Houqiang Li

引用次数: 1

An ensemble of invariant features for person re-identification 一种用于人再识别的不变特征集合

2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-10-01 DOI: 10.1109/MMSP.2015.7340791

Shen-Chi Chen, Young-Gun Lee, Jenq-Neng Hwang, Y. Hung, Jang-Hee Yoo

{"title":"An ensemble of invariant features for person re-identification","authors":"Shen-Chi Chen, Young-Gun Lee, Jenq-Neng Hwang, Y. Hung, Jang-Hee Yoo","doi":"10.1109/MMSP.2015.7340791","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340791","url":null,"abstract":"We propose an ensemble of invariant features for person re-identification. The proposed method requires no domain learning and can effectively overcome the issues created by the variations of human poses and viewpoint between a pair of different cameras. Our ensemble model utilizes both holistic and region-based features. To avoid the misalignment problem, the test human object sample is used to generate multiple virtual samples, by applying slight geometric distortion. The holistic features are extracted from a publically available pre-trained deep convolutional neural network. On the other hand, the region-based features are based on our proposed Two-Way Gaussian Mixture Model Fitting and the Completed Local Binary Pattern texture representations. To make better generalization during the matching without additional learning processes for the feature aggregation, the ensemble scheme combines all three feature distances using distances normalization. The proposed framework achieves robustness against partial occlusion, pose and viewpoint changes. In addition, the experimental results show that our method exceeds the state of the art person re-identification performance based on the challenging benchmark 3DPeS.","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125396453","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: 5

Quality-of-content (QoC)-driven rate allocation for video analysis in mobile surveillance networks 基于内容质量驱动的移动监控网络视频分析速率分配

2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-10-01 DOI: 10.1109/MMSP.2015.7340838

Xiang Chen, Jenq-Neng Hwang, Kuan-Hui Lee, R. Queiroz

{"title":"Quality-of-content (QoC)-driven rate allocation for video analysis in mobile surveillance networks","authors":"Xiang Chen, Jenq-Neng Hwang, Kuan-Hui Lee, R. Queiroz","doi":"10.1109/MMSP.2015.7340838","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340838","url":null,"abstract":"Nowadays, more and more videos are transmitted for video analytics purposes rather than human perceptions. In mobile surveillance networks, a cloud server collects videos delivered from multiple moving cameras and detects suspicious people in all the camera views. However, all the videos recorded by moving cameras such as phone or dash cameras are uploaded through bandwidth-limited wireless networks. Therefore, videos are required to be encoded with high compression ratio to satisfy the total data rate constraint, which may affect the video analyses (e.g., human detection/tracking and action recognition, etc.) performance due to the degraded video decoding qualities at the server side. In this paper, we propose an effective content-driven video source coding rate allocation scheme, which can improve the human detection success rate in mobile surveillance networks under a total data rate constraint. The proposed scheme allocates appropriate amount of data rate to each moving camera based on the corresponding content information (i.e., human detection results). A model of human detection accuracy based on object area and video quality is provided. The rate allocation problem is formulated as a convex optimization problem and can be solved by standard solvers. Simulations with real video sequences demonstrate the effectiveness of our proposed scheme.","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129591794","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: 12

Generalised features for bird vocalisation retrieval in acoustic recordings 声学记录中鸟类发声检索的一般特征

2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-10-01 DOI: 10.1109/MMSP.2015.7340813

Xueyan Dong, Jie Xie, M. Towsey, Jinglan Zhang, P. Roe

{"title":"Generalised features for bird vocalisation retrieval in acoustic recordings","authors":"Xueyan Dong, Jie Xie, M. Towsey, Jinglan Zhang, P. Roe","doi":"10.1109/MMSP.2015.7340813","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340813","url":null,"abstract":"Bioacoustic monitoring has become a significant research topic for species diversity conservation. Due to the development of sensing techniques, acoustic sensors are widely deployed in the field to record animal sounds over a large spatial and temporal scale. With large volumes of collected audio data, it is essential to develop semi-automatic or automatic techniques to analyse the data. This can help ecologists make decisions on how to protect and promote the species diversity. This paper presents generic features to characterize a range of bird species for bird vocalisation retrieval. In the implementation, audio recordings are first converted to spectrograms using short-time Fourier transform, then a ridge detection method is applied to the spectrogram for detecting points of interest. Based on the detected points, a new region representation are explored for describing various bird vocalisations and a local descriptor including temporal entropy, frequency bin entropy and histogram of counts of four ridge directions is calculated for each sub-region. To speed up the retrieval process, indexing is carried out and the retrieved results are ranked according to similarity scores. The experimental results show that our proposed feature set can achieve 0.71 in term of retrieval success rate which outperforms spectral ridge features alone (0.55) and Mel frequency cepstral coefficients (0.36).","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127224346","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

A line segments extraction based undirected graph from 2D laser scans 基于二维激光扫描无向图线段提取的方法

2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-10-01 DOI: 10.1109/MMSP.2015.7340851

Xinzhao Li, Yuehu Liu, Zhenning Niu, Zhichao Cui

引用次数: 2

Large-scale human action recognition with spark 基于火花的大规模人体动作识别

2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-10-01 DOI: 10.1109/MMSP.2015.7340818

Hanli Wang, Xiaobin Zheng, Bo Xiao

引用次数: 11