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

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Thousand to one: An image compression system via cloud search 千比一:通过云搜索的图像压缩系统
2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340872
Chen Zhao, Siwei Ma, Wen Gao
{"title":"Thousand to one: An image compression system via cloud search","authors":"Chen Zhao, Siwei Ma, Wen Gao","doi":"10.1109/MMSP.2015.7340872","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340872","url":null,"abstract":"With the advent of the `big data' era, a huge number of images are produced every day. Traditional image compression methods no longer satisfy the demand to store and transmit them. In this paper, we face this challenge and take advantage of the correlations existing between images to achieve a higher compression rate. We propose an image compression system that encodes each image by referencing its correlated images in the cloud. We first extract features from an image and retrieve its similar image from the massive images in the cloud by comparing these features. Then we preprocess the retrieved picture by applying projective transformation and illumination compensation to obtain multiple reference images of higher prediction accuracy. By taking advantage of the redundancy between the reference images and the current image, we encode the current image through prediction coding techniques. The experimental results demonstrate that the proposed method outperforms JPEG and HEVC intra coding by 61.5% and 21.3% on average, respectively. It has an average compression ratio of over a thousand to one.","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"16 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":"133884388","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: 3
Texture-free large-area depth recovery for planar surfaces 平面无纹理大面积深度恢复
2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340856
Zengqiang Yan, Li Yu, Zixiang Xiong
{"title":"Texture-free large-area depth recovery for planar surfaces","authors":"Zengqiang Yan, Li Yu, Zixiang Xiong","doi":"10.1109/MMSP.2015.7340856","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340856","url":null,"abstract":"This paper presents a texture-free depth enhancement method for large-area depth recovery. The proposed algorithm identifies a large-area depth missing region, and iteratively segments its contour by setting different initial pixels in each iteration. Coordinate transformation is used to analyze the distribution of each contour segment. By examining distributions of all contour segments, statistical histogram analysis is applied in our approach to select contour pixels. Then, selected pixels are projected into the world coordinate system, and multiple linear regression is utilized for surface function approximation. Missing depth values of a large-area depth missing region can be recovered with guidance of the approximated surface function. Quantitative and qualitative evaluations over state-of-the-art depth enhancement methods demonstrate the effectiveness and superiority of our method. Being texture-free, the proposed method has the flexibility of being merged into traditional depth enhancement methods.","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"76 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":"134379893","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: 0
Low bit-rate compression of video and light-field data using coded snapshots and learned dictionaries 低比特率压缩视频和光场数据使用编码快照和学习字典
2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340830
Chandrajit Choudhury, Yellamraju Tarun, Ajit V. Rajwade, S. Chaudhuri
{"title":"Low bit-rate compression of video and light-field data using coded snapshots and learned dictionaries","authors":"Chandrajit Choudhury, Yellamraju Tarun, Ajit V. Rajwade, S. Chaudhuri","doi":"10.1109/MMSP.2015.7340830","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340830","url":null,"abstract":"The method of coded snapshots has been proposed recently for compressive acquisition of video data to overcome the space-time trade-of inherent in video acquisition. The method involves modulation of the light entering the video camera at different time instants during the exposure period by means of a different and randomly generated code pattern at each of those time instants, followed by integration across time, leading to a single coded snapshot image. Given this image and knowledge of the random codes, it is possible to reconstruct the underlying video frames - by means of sparse coding on a suitably learned dictionary. In this paper, we apply a modified version of this idea, proposed formerly in the compressive sensing literature, to the task of compression of videos and light-field data. At low bit rates, we demonstrate markedly better reconstruction fidelity for the same storage costs, in comparison to JPEG2000 and MPEG-4 (H.264) on light-field and video data respectively. Our technique can cope with overlapping blocks of image data, thereby leading to suppression of block artifacts.","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"119 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":"117291665","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: 6
Event recognition in personal photo collections using hierarchical model and multiple features 基于层次模型和多特征的个人照片事件识别
2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340864
Cong Guo, Xinmei Tian
{"title":"Event recognition in personal photo collections using hierarchical model and multiple features","authors":"Cong Guo, Xinmei Tian","doi":"10.1109/MMSP.2015.7340864","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340864","url":null,"abstract":"With the proliferation of digital cameras and mobile devices, people are taking many more photos than ever before. The explosive growth of personal photos leads to problems of photo organization and management. There is a growing need for tools to automatically manage photo collections. Recognizing events in photo collections is one efficient way to organize photos. The use of textual event labels can allow us to categorize and locate an event without browsing through an entire photo collection. Most existing research on this topic focuses on recognizing events from single photos and only a few studies have examined event recognition in personal photo collections. In this paper, we propose a hierarchical model to recognize events in personal photo collections using multiple features, including time, objects, and scenes. Since some events are more difficult to identify and categorize, ambiguous events require fine event classifiers, while the coarse categories of the events can be sufficiently organized with a coarse event classifier. We evaluate our coarse-to-fine hierarchical model on a real-world dataset consisting of personal photo collections, and our model achieves promising results.","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"5 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":"133583022","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
Kinect-based non-intrusive human gait analysis and visualization 基于运动学的非侵入式人体步态分析与可视化
2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340804
Nguyen-Luc Dao, Yuzhe Zhang, Jianmin Zheng, Jianfei Cai
{"title":"Kinect-based non-intrusive human gait analysis and visualization","authors":"Nguyen-Luc Dao, Yuzhe Zhang, Jianmin Zheng, Jianfei Cai","doi":"10.1109/MMSP.2015.7340804","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340804","url":null,"abstract":"Home healthcare becomes more and more important with the increased aging population. The advent of various low-cost sensing devices makes it tempting to develop low-cost, non-intrusive systems to monitor the variations of human being. In this paper, we describe a Kinect-based gait analysis and visualization system as a case study in this direction. The system uses depth images and skeleton captured by the Kinect to generate a BVH file recording the motion information, extracts features of gait for detecting abnormal gait, and customizes the 3D body model for personalized motion visualization. Compared to previous work in this area, the proposed system has advantages since it integrates gait classification and visualization which may bring new possibilities in healthcare. The experiments show that our proposed system achieves accurate gait classification as well as flexible personalized 3D visualization.","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"59 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":"121929530","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
Adaptive guided image filter for improved in-loop filtering in video coding 改进视频编码环内滤波的自适应引导图像滤波
2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340842
Cheng Chen, Z. Miao, B. Zeng
{"title":"Adaptive guided image filter for improved in-loop filtering in video coding","authors":"Cheng Chen, Z. Miao, B. Zeng","doi":"10.1109/MMSP.2015.7340842","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340842","url":null,"abstract":"This paper proposes a new adaptive sharpening filter based on guided image filter and improves HEVC's in-loop filter architecture by embedding sharpening filter between deblocking filter and SAO. The proposed algorithm classifies pixels of a frame into several groups according to uniform quantization of each pixel's Sum-Modified-Laplacian value and assigns identical optimal filtering parameters to the pixels belonging to the same group based on rate-distortion optimization. Simulation results show that our proposed algorithm achieves 0.7% on average and up to 8% BD-rate reduction with respect to the original HEVC in-loop filtering method. Encoding time increases slightly by about 15% and decoding time increases by 70% on average without special optimization of C++ program integrated in HM-16.5.","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"11 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":"132460815","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: 0
Real time and scene invariant crowd counting: Across a line or inside a region 实时和场景不变人群计数:跨越一条线或在一个区域内
2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340812
Mingjie Deng, Yi Xu, Pufan Jiang, Xiaokang Yang
{"title":"Real time and scene invariant crowd counting: Across a line or inside a region","authors":"Mingjie Deng, Yi Xu, Pufan Jiang, Xiaokang Yang","doi":"10.1109/MMSP.2015.7340812","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340812","url":null,"abstract":"In this paper, we propose a blob-based method of crowd counting across a line of interest (LOI), which can be further extended to counting inside a region of interest (ROI). Firstly, we detect moving blobs in which low-level features are extracted and grouped. Since features vary with different walking pace, blob velocity is estimated using optical flow, and principal velocity component is further extracted in case of the interference of local articulated motion. Besides, spatial normalization is implemented to compensate for different image depth and different scenes. Finally, we apply Gaussian Process Regression to model the global linear and local nonlinear relationships between the extracted features and crowd counts. The experimental results demonstrate that the proposed method has good applicability to both LOI and ROI crowd counting. As compared with the state-of-the-art methods, our method achieves higher counting accuracy over two representative datasets and meanwhile processes much faster.","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"34 6 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":"124301103","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
Human Action recognition from depth videos using multi-projection based representation 基于多投影表示的深度视频人体动作识别
2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340879
Chien-Quang Le, T. Ngo, Duy-Dinh Le, S. Satoh, D. Duong
{"title":"Human Action recognition from depth videos using multi-projection based representation","authors":"Chien-Quang Le, T. Ngo, Duy-Dinh Le, S. Satoh, D. Duong","doi":"10.1109/MMSP.2015.7340879","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340879","url":null,"abstract":"In this paper, a novel method for human action recognition from depth videos is proposed. We project 3D data on to multiple 2D-planes from which dense trajectories features are extracted. In the training stage, for each projection, a classifier is trained using the training data. In the testing stage, for each test video, the multiple trained classifiers are applied and the predicted scores are combined for final decision. We propose a greedy-based method to select a subset of the trained classifiers for optimal combination. Experiments on the MSR Action 3D dataset show that the proposed method outperforms the baseline method that does not use multi-projection-based features.","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"54 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":"126841502","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: 3
Pedestrian detection based on Region Proposal Fusion 基于区域建议融合的行人检测
2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-10-01 DOI: 10.1109/MMSP.2015.7340847
Bin Wang, Sheng Tang, Ruizhen Zhao, Wu Liu, Yigang Cen
{"title":"Pedestrian detection based on Region Proposal Fusion","authors":"Bin Wang, Sheng Tang, Ruizhen Zhao, Wu Liu, Yigang Cen","doi":"10.1109/MMSP.2015.7340847","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340847","url":null,"abstract":"Almost all existing state-of-the-art pedestrian detection methods use combination of hand-crafted features, which cannot well handle the particular challenges in real-world situation. In this paper, we take advantage of Regions with Convolution Neural Networks features (R-CNN) to extract more robust pedestrian features for effective pedestrian detection in complicated environments. To further improve the performance: 1) we propose a Region Proposal Fusion algorithm to get effective region proposals since after careful observation, we found that the quality of region proposals is crucially important for detection performance. 2) we exploit a pedestrian detection expansion method based on image retrieval with color moment features due to R-CNN's requirements of large number of training samples to avoid overfitting. Consequently, the final average miss rate is greatly reduced to 23% in the INRIA pedestrian detection dataset, which is much (23%) lower than that of original HOG (46%).","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"29 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":"125695095","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: 8
A robust method for aligning large-photometric-variation and noisy images 一种校正大光度变化和噪声图像的鲁棒方法
2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-10-01 DOI: 10.1109/MMSP.2015.7340833
Shiqian Wu, Wangming Xu, Jun Jiang, Yimin Qiu, L. Zeng
{"title":"A robust method for aligning large-photometric-variation and noisy images","authors":"Shiqian Wu, Wangming Xu, Jun Jiang, Yimin Qiu, L. Zeng","doi":"10.1109/MMSP.2015.7340833","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340833","url":null,"abstract":"We propose a novel robust method to accurately align large-photometric-variation and noisy images emerged from high dynamic range (HDR) imaging. First, the keypoints are detected from optimal multi-binary images to eliminate the noise and photometric effects. The feature descriptor, which is translation-, rotation- and scale-invariant and robust to noise and photometric changes, is then developed, and feature matching is implemented efficiently by use of the structure information of the keypoints. Finally, mutual information (MI) is employed in the RANSAC method for homography estimation and performance assessment, which makes the results accurate and stable. Experiments carried out on synthesized images demonstrate that the proposed method is much more robust to both photometric changes and noise than the SIFT method, the state-of-the-art alignment method.","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"551 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":"115959486","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
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