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

VRank: Voting system on Ranking model for human age estimation VRank:基于排名模型的投票系统，用于人类年龄估计

2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340789

Tekoing Lim, K. Hua, Hong-Cyuan Wang, Kai Zhao, Min-Chun Hu, Wen-Huang Cheng

{"title":"VRank: Voting system on Ranking model for human age estimation","authors":"Tekoing Lim, K. Hua, Hong-Cyuan Wang, Kai Zhao, Min-Chun Hu, Wen-Huang Cheng","doi":"10.1109/MMSP.2015.7340789","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340789","url":null,"abstract":"Ranking algorithms have proven the potential for human age estimation. Currently, a common paradigm is to compare the input face with reference faces of known age to generate a ranking relation whereby the first-rank reference is exploited for labeling the input face. In this paper, we proposed a framework to improve upon the typical ranking model, called Voting system on Ranking model (VRank), by leveraging relational information (comparative relations, i.e. if the input face is younger or older than each of the references) to make a more robust estimation. Our approach has several advantages: firstly, comparative relations can be explicitly involved to benefit the estimation task; secondly, few incorrect comparisons will not influence much the accuracy of the result, making this approach more robust than the conventional approach; finally, we propose to incorporate the deep learning architecture for training, which extracts robust facial features for increasing the effectiveness of classification. In comparison to the best results from the state-of-the-art methods, the VRank showed a significant outperformance on all the benchmarks, with a relative improvement of 5.74% ~ 69.45% (FG-NET), 19.09% ~ 68.71% (MORPH), and 0.55% ~ 17.73% (IoG).","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128971721","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

Future buffer based adaptation for VBR video streaming over HTTP 未来基于缓冲区的基于HTTP的VBR视频流适应

2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340878

T. Vu, Hung T. Le, Duc V. Nguyen, N. P. Ngoc, T. Thang

引用次数: 5

RIFO: Restoring images with fence occlusions 复原带有栅栏遮挡的图像

2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340834

Jingyu Yang, J. Wang, Leijie Liu, Chunping Hou

引用次数: 6

Reducing Wedgelet lookup table size with down-sampling for depth map coding in 3D-HEVC 减少Wedgelet查找表的大小与下采样深度图编码在3D-HEVC

2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340849

S. Ma, Yong Wang, Ce Zhu, Yongbing Lin, Jianhua Zheng

引用次数: 12

Learning query and image similarities with listwise supervision 用列表监督学习查询和图像相似度

2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340793

Yuan Liu, Zhongchao Shi, Zhenhua Liu, Xue Li, G. Wang

{"title":"Learning query and image similarities with listwise supervision","authors":"Yuan Liu, Zhongchao Shi, Zhenhua Liu, Xue Li, G. Wang","doi":"10.1109/MMSP.2015.7340793","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340793","url":null,"abstract":"One of the fundamental problems in image search is to learn the ranking functions, i.e., similarity between the textual query and visual image. A number of research paradigms, ranging from feature-based vector model to image ranker learning, have been applied to measure query-image similarity. However, most of the existing similarity learning methods either depend on surrounding texts for ranking images or learn image rankers to satisfy pairwise or tripletwise supervision. In this paper, we propose to leverage listwise supervision into a principled click-through-based query-image similarity learning framework. In particular, the algorithm utilizes click counts for each image in response to a query to get a ranking list. The ranking information is represented by a set of rank triplets that can be used to assess the quality of the ranking list. The image ranking problem is then solved efficiently by learning two linear projections for query and image space respectively, through maximizing the ranking quality over all the training data. When the two linear projections are learnt, query-image similarity can be directly computed by dot product on this projected subspace. On a large-scale click-through-based image dataset with 11.7 million queries and one million images, our learnt model via listwise supervision is shown to be powerful for keyword-based image search with superior performance over several state-of-the-art methods.","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"1067 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123164999","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: 1

Rating prediction via exploring service reputation 通过探索服务声誉进行评级预测

2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340814

Xiaojiang Lei, Xueming Qian

引用次数: 25

Accurate silhouette extraction of multiple moving objects for free viewpoint sports video synthesis 准确的轮廓提取多个运动对象的自由视点体育视频合成

2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340803

Qiang Yao, Hiroshi Sankoh, H. Sabirin, S. Naito

引用次数: 7

Spatial-temporal correlation for trajectory based action video retrieval 基于轨迹的动作视频检索的时空相关性

2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340811

Xi Shen, Lelin Zhang, Zhiyong Wang, D. Feng

{"title":"Spatial-temporal correlation for trajectory based action video retrieval","authors":"Xi Shen, Lelin Zhang, Zhiyong Wang, D. Feng","doi":"10.1109/MMSP.2015.7340811","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340811","url":null,"abstract":"The bag-of-visual-words model has been widely utilized for content based image and video retrieval due to its scalability. In this paper, we extend this model for human action video retrieval. We adopt dense trajectory features which are able to achieve the state-of-the-art performance on action recognition, while most of the existing video retrieval methods utilize descriptors of local interest points. In order to improve similarity measurement between bag-of-visual-words model based representation, we propose to discover and incorporate spatial-temporal correlation (STC) among the trajectories in a given query video. The spatial-temporal correlation consists of spatial proximity and temporal consistence among trajectories, which is capable of strengthening discriminative power among visual words. Note that such query focused spatial-temporal correlation makes our method dynamic for different queries and is able to improve retrieval performance without significantly increasing the size of a visual vocabulary. The experimental results on an action video dataset demonstrate that our proposed method outperforms other similar methods.","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"80 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134330735","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: 2

Dictionary design for sensor network localization via block-sparsity 基于块稀疏的传感器网络定位词典设计

2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340843

Alessandro Bay, Diego Carrera, S. Fosson, P. Fragneto, Marco Grella, C. Ravazzi, E. Magli

引用次数: 2

Fast learning discriminative dictionaries for large-scale visual recognition 面向大规模视觉识别的快速学习判别字典

2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340876

Tianyi Zhao, Yanyun Qu, Jianping Fan

引用次数: 1