2015 IEEE International Conference on Multimedia and Expo (ICME)最新文献_第4页

Improved performance of inverse halftoning algorithms via coupled dictionaries 利用耦合字典改进反半调算法的性能

2015 IEEE International Conference on Multimedia and Expo (ICME) Pub Date : 2015-08-06 DOI: 10.1109/ICME.2015.7177457

P. Freitas, Mylène C. Q. Farias, Aleteia P. F. Araujo

引用次数: 0

Detecting abnormal behaviors in surveillance videos based on fuzzy clustering and multiple Auto-Encoders 基于模糊聚类和多自编码器的监控视频异常行为检测

2015 IEEE International Conference on Multimedia and Expo (ICME) Pub Date : 2015-08-06 DOI: 10.1109/ICME.2015.7177459

Zhengying Chen, Yonghong Tian, Wei Zeng, Tiejun Huang

{"title":"Detecting abnormal behaviors in surveillance videos based on fuzzy clustering and multiple Auto-Encoders","authors":"Zhengying Chen, Yonghong Tian, Wei Zeng, Tiejun Huang","doi":"10.1109/ICME.2015.7177459","DOIUrl":"https://doi.org/10.1109/ICME.2015.7177459","url":null,"abstract":"In this paper, we present a novel framework to detect abnormal behaviors in surveillance videos by using fuzzy clustering and multiple Auto-Encoders (FMAE). As detecting abnormal behaviors is often treated as an unsupervised task, how to describe normal patterns becomes the key point. Considering there are many types of normal behaviors in the daily life, we use the fuzzy clustering technique to roughly divide the training samples into several clusters so that each cluster stands for a normal pattern. Then we deploy multiple Auto-Encoders to estimate these different types of normal behaviors from weighted samples. When testing on an unknown video, our framework can predict whether it contains abnormal behaviors or not by summarizing the reconstruction cost through each Auto-Encoder. Since there are always lots of redundancies in the surveillance video, Auto-Encoder is a pretty good tool to capture common structures of normal video sequences automatically as well as estimate normal patterns. The experimental results show that our approach achieves good performance on three public video analysis datasets and statistically outperforms the state-of-the-art approaches under some scenes.","PeriodicalId":146271,"journal":{"name":"2015 IEEE International Conference on Multimedia and Expo (ICME)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126640475","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: 23

Facial expression preserving privacy protection using image melding 基于图像融合的面部表情隐私保护

2015 IEEE International Conference on Multimedia and Expo (ICME) Pub Date : 2015-08-06 DOI: 10.1109/ICME.2015.7177394

Yuta Nakashima, Tatsuya Koyama, N. Yokoya, N. Babaguchi

引用次数: 21

An adaptive PEE-based reversible data hiding scheme exploiting referential prediction-errors 一种利用参考预测误差的自适应基于pee的可逆数据隐藏方案

2015 IEEE International Conference on Multimedia and Expo (ICME) Pub Date : 2015-08-06 DOI: 10.1109/ICME.2015.7177512

Fei Peng, Xiaolong Li, Bin Yang

引用次数: 2

Real-time face detection in Full HD images exploiting both embedded CPU and GPU 利用嵌入式CPU和GPU的全高清图像实时人脸检测

2015 IEEE International Conference on Multimedia and Expo (ICME) Pub Date : 2015-08-06 DOI: 10.1109/ICME.2015.7177522

Chanyoung Oh, Saehanseul Yi, Youngmin Yi

引用次数: 20

Machine learning based rate adaptation with elastic feature selection for HTTP-based streaming 基于机器学习的基于http流的速率自适应弹性特征选择

2015 IEEE International Conference on Multimedia and Expo (ICME) Pub Date : 2015-08-06 DOI: 10.1109/ICME.2015.7177418

Yu-Lin Chien, K. Lin, Ming-Syan Chen

{"title":"Machine learning based rate adaptation with elastic feature selection for HTTP-based streaming","authors":"Yu-Lin Chien, K. Lin, Ming-Syan Chen","doi":"10.1109/ICME.2015.7177418","DOIUrl":"https://doi.org/10.1109/ICME.2015.7177418","url":null,"abstract":"Dynamic Adaptive Streaming over HTTP (DASH) has become an emerging application nowadays. Video rate adaptation is a key to determine the video quality of HTTP-based media streaming. Recent works have proposed several algorithms that allow a DASH client to adapt its video encoding rate to network dynamics. While network conditions are typically affected by many different factors, these algorithms however usually consider only a few representative information, e.g., predicted available bandwidth or fullness of its playback buffer. In addition, the error in bandwidth estimation could significantly degrade their performance. Therefore, this paper presents Machine Learning-based Adaptive Streaming over HTTP (MLASH), an elastic framework that exploits a wide range of useful network-related features to train a rate classification model. The distinct properties of MLASH are that its machine learning-based framework can be incorporated with any existing adaptation algorithm and utilize big data characteristics to improve prediction accuracy. We show via trace-based simulations that machine learning-based adaptation can achieve a better performance than traditional adaptation algorithms in terms of their target quality of experience (QoE) metrics.","PeriodicalId":146271,"journal":{"name":"2015 IEEE International Conference on Multimedia and Expo (ICME)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131693217","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: 22

Refining graph matching using inherent structure information 利用固有结构信息优化图匹配

2015 IEEE International Conference on Multimedia and Expo (ICME) Pub Date : 2015-08-06 DOI: 10.1109/ICME.2015.7177409

Wenzhao Li, Yi-Zhe Song, A. Cavallaro

引用次数: 0

Coupled dictionary learning and feature mapping for cross-modal retrieval 跨模态检索的耦合字典学习和特征映射

2015 IEEE International Conference on Multimedia and Expo (ICME) Pub Date : 2015-08-06 DOI: 10.1109/ICME.2015.7177396

Xing Xu, Atsushi Shimada, R. Taniguchi, Li He

{"title":"Coupled dictionary learning and feature mapping for cross-modal retrieval","authors":"Xing Xu, Atsushi Shimada, R. Taniguchi, Li He","doi":"10.1109/ICME.2015.7177396","DOIUrl":"https://doi.org/10.1109/ICME.2015.7177396","url":null,"abstract":"In this paper, we investigate the problem of modeling images and associated text for cross-modal retrieval tasks such as text-to-image search and image-to-text search. To make the data from image and text modalities comparable, previous cross-modal retrieval methods directly learn two projection matrices to map the raw features of the two modalities into a common subspace, in which cross-modal data matching can be performed. However, the different feature representations and correlation structures of different modalities inhibit these methods from efficiently modeling the relationships across modalities through a common subspace. To handle the diversities of different modalities, we first leverage the coupled dictionary learning method to generate homogeneous sparse representations for different modalities by associating and jointly updating their dictionaries. We then use a coupled feature mapping scheme to project the derived sparse representations from different modalities into a common subspace in which cross-modal retrieval can be performed. Experiments on a variety of cross-modal retrieval tasks demonstrate that the proposed method outperforms the state-of-the-art approaches.","PeriodicalId":146271,"journal":{"name":"2015 IEEE International Conference on Multimedia and Expo (ICME)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130951298","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: 32

Graph regularized non-negative local coordinate factorization with pairwise constraints for image representation 具有成对约束的图像正则化非负局部坐标分解

2015 IEEE International Conference on Multimedia and Expo (ICME) Pub Date : 2015-08-06 DOI: 10.1109/ICME.2015.7177386

Yangcheng He, Hongtao Lu, Bao-Liang Lu

引用次数: 1

Temporally consistent region-based video exposure correction 时间一致的基于区域的视频曝光校正

2015 IEEE International Conference on Multimedia and Expo (ICME) Pub Date : 2015-08-06 DOI: 10.1109/ICME.2015.7177382

Xuan Dong, Lu Yuan, Weixin Li, A. Yuille

引用次数: 5