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2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP)最新文献

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A secure mobile multimedia content sharing service (SMM-CSS) 安全移动多媒体内容共享服务(SMM-CSS)
2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2012-11-12 DOI: 10.1109/MMSP.2012.6343449
Min-Jen Tsai, Ying-Ting Chuang, Huai-Che Hung, Po-Wei Su
{"title":"A secure mobile multimedia content sharing service (SMM-CSS)","authors":"Min-Jen Tsai, Ying-Ting Chuang, Huai-Che Hung, Po-Wei Su","doi":"10.1109/MMSP.2012.6343449","DOIUrl":"https://doi.org/10.1109/MMSP.2012.6343449","url":null,"abstract":"The increasing number of smart phone users together with the mobile communication and social network technology improvement lead to multi-functional services, such as realtime multimedia content sharing, Location-Based Service (LBS) and Geo-Social Networks (GeoSNs). However, the issues of multimedia content sharing with location-related privacy in GeoSNs start to be investigated and the security solution is yet to be found. Therefore, we propose a secure mobile multimedia content sharing service (SMM-CSS) which includes mobile users (MUs) and GeoSNs. To address the security concern, SMM-CSS has applied the Network Coding (NC) and Homomorphic Encryption (HE) techniques to support the secure multimedia transmission. Under such mechanism, personal location and social network relationship can be managed for secure communication. In addition, the architecture can be easily implemented to the existing GeoSNs.","PeriodicalId":325274,"journal":{"name":"2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP)","volume":"19 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125971538","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
Cross-domain object recognition by output kernel learning 基于输出核学习的跨域目标识别
2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2012-11-12 DOI: 10.1109/MMSP.2012.6343471
Zhenyu Guo, Z. J. Wang
{"title":"Cross-domain object recognition by output kernel learning","authors":"Zhenyu Guo, Z. J. Wang","doi":"10.1109/MMSP.2012.6343471","DOIUrl":"https://doi.org/10.1109/MMSP.2012.6343471","url":null,"abstract":"It is of great importance to investigate the domain adaptation problem as vision data is now available from a variety of sources. For adapting a classifier, the first problem is how to choose `source' domain. The key issue here is measuring domain similarity. In this paper, we present one of the first studies on `domain similarity' measure in the context of object recognition. We introduce an output kernel divergence as a similarity measure between different data domains, and propose using it as a criterion for domain selection for better recognition accuracy. We also propose a novel domain adaptation method using a vector-valued function with learned output kernels. Fundamentally different from existing work, we focus on the shift in the output kernel space, instead of handling the distribution shift in the input feature space. In addition, those previous methods could also be applied together with ours to improve the performance further. We demonstrate the ability of the proposed model to select and adapt between different domains, and report the state-of-art results on a benchmark data set.","PeriodicalId":325274,"journal":{"name":"2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115144519","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
Motion estimation using block overlap minimization 使用块重叠最小化的运动估计
2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2012-09-01 DOI: 10.1109/MMSP.2012.6343438
Michael Santoro, G. Al-Regib, Y. Altunbasak
{"title":"Motion estimation using block overlap minimization","authors":"Michael Santoro, G. Al-Regib, Y. Altunbasak","doi":"10.1109/MMSP.2012.6343438","DOIUrl":"https://doi.org/10.1109/MMSP.2012.6343438","url":null,"abstract":"This paper presents a block-overlap-based method for handling the ill-posed nature of motion estimation. The proposed method uses the volume of motion-compensated block overlap as an additional term in minimizing the overall energy. By reducing the amount of block overlap, the proposed method results in a significant improvement in the quality of the motion field. Experimental results show that the proposed method outperforms several existing methods in the literature in terms of motion vector (MV) and interpolation quality. In terms of interpolation quality, our algorithm outperforms all other block-based methods as well as several complex optical flow methods. In addition, it is the fastest non-GPU implementation at the time of this writing.","PeriodicalId":325274,"journal":{"name":"2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP)","volume":"51 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116822963","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: 4
Examplar-based object posture super-resolution using manifold learning 使用流形学习的基于实例的物体姿态超分辨
2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2012-09-01 DOI: 10.1109/MMSP.2012.6343430
Chih-Hung Ling, Chia-Wen Lin, Chiou-Ting Hsu, H. Liao
{"title":"Examplar-based object posture super-resolution using manifold learning","authors":"Chih-Hung Ling, Chia-Wen Lin, Chiou-Ting Hsu, H. Liao","doi":"10.1109/MMSP.2012.6343430","DOIUrl":"https://doi.org/10.1109/MMSP.2012.6343430","url":null,"abstract":"This paper proposes a learning-based approach to increase the temporal resolutions of human motion sequences. Given a set of high resolution motion sequences, our idea is first to learn the motion tendency from this learning dataset and then synthesize new postures for the low-resolution sequence according to the learned motion tendency. We summarize the proposed framework in the following steps: (1) Each motion sequence is first projected into a low-dimension manifold space, where the local distance between postures could be better preserved. We then represent each of the projected motion sequences as a motion trajectory. (2) Next, motion priors learned from the HR training sequences are used to reconstruct the motion trajectory for the input sequence. (3) Finally, we use the reconstructed motion trajectory combined with object inpainting technique to generate the final result. Our experimental results demonstrate the effectiveness of the proposed method, and also show its outperformance over existing approaches.","PeriodicalId":325274,"journal":{"name":"2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116811087","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
Similar images compression based on DCT pyramid multi-level low frequency template 基于DCT金字塔多层低频模板的相似图像压缩
2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2012-09-01 DOI: 10.1109/MMSP.2012.6343450
Sijin Li, O. Au, R. Zou, Lin Sun, W. Dai
{"title":"Similar images compression based on DCT pyramid multi-level low frequency template","authors":"Sijin Li, O. Au, R. Zou, Lin Sun, W. Dai","doi":"10.1109/MMSP.2012.6343450","DOIUrl":"https://doi.org/10.1109/MMSP.2012.6343450","url":null,"abstract":"Medical imaging applications produce a huge amount of similar images. Instead of compressing each image individually, set redundancy compression (SRC) methods remove the inter image redundancy and reduce storage. However, in the previous SRC methods - MMD, MMP and Centroid methods, the prediction templates for extracting set redundancy are not very efficient, especially when image sets are very large with several clusters. In this paper, inspired by face recognition techniques, a novel lossless SRC method is derived based onDCT pyramid multi-level low frequency template. The approximation subband is used as a prediction template for each image to calculate the residue. Intra prediction is also used to reduce the entropy of the residues. Experiments with 3 sets of MR brain images demonstrate the efficiency of our proposed algorithm in respect to bits/pixel (bpp).","PeriodicalId":325274,"journal":{"name":"2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130176888","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
Multiple compression detection for video sequences 视频序列的多重压缩检测
2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2012-09-01 DOI: 10.1109/MMSP.2012.6343425
S. Milani, Paolo Bestagini, M. Tagliasacchi, S. Tubaro
{"title":"Multiple compression detection for video sequences","authors":"S. Milani, Paolo Bestagini, M. Tagliasacchi, S. Tubaro","doi":"10.1109/MMSP.2012.6343425","DOIUrl":"https://doi.org/10.1109/MMSP.2012.6343425","url":null,"abstract":"Nowadays, thanks to the increasingly availability of powerful processors and user friendly applications, the editing of video sequences is becoming more and more frequent. Moreover, after each editing step, any video object is almost always encoded in order to store it using a less amount of memory. For this reason, inferring the number of compression steps that have been applied to such a multimedia object is an important clue in order to assess its authenticity. In this paper we propose a method to recover the number of compression steps applied to a video sequence. In order to accomplish this goal, we make use of a classifier based on multiple Support Vector Machines (SVM) exploiting the Benford's law. Indeed, the feature vectors used to train and test the SVM are based on the statistics of the most significant digit of quantized transform coefficients. The proposed method is tested with a generic hybrid video encoder combining motion-compensation and block coding. Results show that this method is able to discriminate up to three compression stages with high accuracy.","PeriodicalId":325274,"journal":{"name":"2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129877062","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: 52
Motion robust rain detection and removal from videos 运动健壮的雨检测和去除视频
2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2012-09-01 DOI: 10.1109/MMSP.2012.6343435
Xinwei Xue, Xin Jin, Chenyuan Zhang, S. Goto
{"title":"Motion robust rain detection and removal from videos","authors":"Xinwei Xue, Xin Jin, Chenyuan Zhang, S. Goto","doi":"10.1109/MMSP.2012.6343435","DOIUrl":"https://doi.org/10.1109/MMSP.2012.6343435","url":null,"abstract":"Weather such as rain and snow cause difficulties in processing the videos captured. Since the appearance of rain drops can affect the performance of human tracking and reduce the efficiency of video compression, detection and removal of rain is a challenging problem in outdoor surveillance systems. In this paper, we propose a new algorithm for rain detection, which is based on joint spatial and wavelet domain features. This approach is robust to the videos with moving objects in the rain. Experimental results demonstrated its better performance in comparison with the existing approaches in the subjective quality.","PeriodicalId":325274,"journal":{"name":"2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129352953","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: 20
A tridirectional method for corticomuscular coupling analysis in Parkinson's disease 帕金森病皮质肌耦合分析的三向方法
2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2012-09-01 DOI: 10.1109/MMSP.2012.6343460
Xun Chen, Z. J. Wang, M. McKeown
{"title":"A tridirectional method for corticomuscular coupling analysis in Parkinson's disease","authors":"Xun Chen, Z. J. Wang, M. McKeown","doi":"10.1109/MMSP.2012.6343460","DOIUrl":"https://doi.org/10.1109/MMSP.2012.6343460","url":null,"abstract":"Corticomuscular coupling analysis based on multiple datasets such as electroencephalography (EEG) and electromyography (EMG) signals provides a useful tool for understanding the underlying mechanisms of human motor control systems. In this work, we propose a tridirectional statistical modeling and analysis method to identify the coupling relationships between three types of datasets. Different from conventional approaches where only two datasets are considered and the interest is to interpret one dataset by another in a unidirectional fashion, the goal in this paper is to model three data spaces simultaneously in a tridirectional fashion. To address the intersubject variability concern in real-world medical applications, we further propose a group analysis framework based on the proposed method and apply it to concurrent EEG, EMG and behavior signals collected from 8 normal subjects and 9 patients with Parkinson's disease (PD) performing a dynamic motor task. The results demonstrate highly correlated temporal patterns among the three types of signals and meaningful spatial activation patterns. The proposed approach is a promising technique for performing multi-subject and multi-modal data analysis.","PeriodicalId":325274,"journal":{"name":"2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115500490","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
Modified distortion redistribution problem for High Efficiency Video Coding(HEVC) 高效视频编码(HEVC)中的改进失真再分配问题
2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2012-09-01 DOI: 10.1109/MMSP.2012.6343454
Lin Sun, O. Au, W. Dai, R. Zou, Sijin Li
{"title":"Modified distortion redistribution problem for High Efficiency Video Coding(HEVC)","authors":"Lin Sun, O. Au, W. Dai, R. Zou, Sijin Li","doi":"10.1109/MMSP.2012.6343454","DOIUrl":"https://doi.org/10.1109/MMSP.2012.6343454","url":null,"abstract":"Adaptive quantization matrix design for different block sizes is one of the possible methods to improve the RD performance in video coding and has recently attracted the focus of many researchers. In this paper, we first analyze the shortcomings of the evenly distributed distortion method which was proposed recently. In order to tackle these problems, we propose two modified methods, method I with relaxed distortion constraints and method II is iterative boundary distortion minimization problem considering variance adaptively. Both problems can be solved using convex optimization effectively and efficiently. Simulations have been conducted based on HM4.0, which is the reference software of the latest High Efficiency Video Coding (HEVC). Simulation results show the effect of our proposed methods. Both methods show their significance when evaluated by RD performance.","PeriodicalId":325274,"journal":{"name":"2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132078667","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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