People re-identification is a challenging problem in surveillance and forensics and it aims at associating multiple instances of the same person which have been acquired from different points of view and after a temporal gap. Image-based appearance features are usually adopted but, in addition to their intrinsically low discriminability, they are subject to perspective and view-point issues. We propose to completely change the approach by mapping local descriptors extracted from RGB-D sensors on a 3D body model for creating a view-independent signature. An original bone-wise color descriptor is generated and reduced with PCA to compute the person signature. The virtual bone set used to map appearance features is learned using a recursive splitting approach. Finally, people matching for re-identification is performed using the Relaxed Pairwise Metric Learning, which simultaneously provides feature reduction and weighting. Experiments on a specific dataset created with the Microsoft Kinect sensor and the OpenNi libraries prove the advantages of the proposed technique with respect to state of the art methods based on 2D or non-articulated 3D body models.
{"title":"Learning articulated body models for people re-identification","authors":"Davide Baltieri, R. Vezzani, R. Cucchiara","doi":"10.1145/2502081.2502147","DOIUrl":"https://doi.org/10.1145/2502081.2502147","url":null,"abstract":"People re-identification is a challenging problem in surveillance and forensics and it aims at associating multiple instances of the same person which have been acquired from different points of view and after a temporal gap. Image-based appearance features are usually adopted but, in addition to their intrinsically low discriminability, they are subject to perspective and view-point issues. We propose to completely change the approach by mapping local descriptors extracted from RGB-D sensors on a 3D body model for creating a view-independent signature. An original bone-wise color descriptor is generated and reduced with PCA to compute the person signature. The virtual bone set used to map appearance features is learned using a recursive splitting approach. Finally, people matching for re-identification is performed using the Relaxed Pairwise Metric Learning, which simultaneously provides feature reduction and weighting. Experiments on a specific dataset created with the Microsoft Kinect sensor and the OpenNi libraries prove the advantages of the proposed technique with respect to state of the art methods based on 2D or non-articulated 3D body models.","PeriodicalId":20448,"journal":{"name":"Proceedings of the 21st ACM international conference on Multimedia","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82066897","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}
{"title":"Session details: Keynote address","authors":"David A. Shamma","doi":"10.1145/3245284","DOIUrl":"https://doi.org/10.1145/3245284","url":null,"abstract":"","PeriodicalId":20448,"journal":{"name":"Proceedings of the 21st ACM international conference on Multimedia","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86824028","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}
This paper studies the use of everyday words to describe images. The common saying has it that 'a picture is worth a thousand words', here we ask which thousand? The proliferation of tagged social multimedia data presents a challenge to understanding collective tag-use at large scale -- one can ask if patterns from photo tags help understand tag-tag relations, and how it can be leveraged to improve visual search and recognition. We propose a new method to jointly analyze three distinct visual knowledge resources: Flickr, ImageNet/WordNet, and ConceptNet. This allows us to quantify the visual relevance of both tags learn their relationships. We propose a novel network estimation algorithm, Inverse Concept Rank, to infer incomplete tag relationships. We then design an algorithm for image annotation that takes into account both image and tag features. We analyze over 5 million photos with over 20,000 visual tags. The statistics from this collection leads to good results for image tagging, relationship estimation, and generalizing to unseen tags. This is a first step in analyzing picture tags and everyday semantic knowledge. Potential other applications include generating natural language descriptions of pictures, as well as validating and supplementing knowledge databases.
{"title":"Picture tags and world knowledge: learning tag relations from visual semantic sources","authors":"Lexing Xie, Xuming He","doi":"10.1145/2502081.2502113","DOIUrl":"https://doi.org/10.1145/2502081.2502113","url":null,"abstract":"This paper studies the use of everyday words to describe images. The common saying has it that 'a picture is worth a thousand words', here we ask which thousand? The proliferation of tagged social multimedia data presents a challenge to understanding collective tag-use at large scale -- one can ask if patterns from photo tags help understand tag-tag relations, and how it can be leveraged to improve visual search and recognition. We propose a new method to jointly analyze three distinct visual knowledge resources: Flickr, ImageNet/WordNet, and ConceptNet. This allows us to quantify the visual relevance of both tags learn their relationships. We propose a novel network estimation algorithm, Inverse Concept Rank, to infer incomplete tag relationships. We then design an algorithm for image annotation that takes into account both image and tag features. We analyze over 5 million photos with over 20,000 visual tags. The statistics from this collection leads to good results for image tagging, relationship estimation, and generalizing to unseen tags. This is a first step in analyzing picture tags and everyday semantic knowledge. Potential other applications include generating natural language descriptions of pictures, as well as validating and supplementing knowledge databases.","PeriodicalId":20448,"journal":{"name":"Proceedings of the 21st ACM international conference on Multimedia","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88380513","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}
Inter-Destination Media Synchronization (IDMS) is essential for enabling pleasant shared media experiences. The goal of my PhD thesis is to design, develop and evaluate an advanced RTP/RTCP-based IDMS solution fitting the requirements of the emerging distributed media consumption paradigm. In particular, standard compliant extensions to RTCP are being specified to allow for an accurate, adaptive and dynamic IDMS control when using RTP for streaming media. Moreover, the feasibility and suitability of several architectural schemes for exchanging the IDMS information, algorithms for allowing a dynamic IDMS monitoring and control, as well as adjustment techniques are being investigated. Objective and subjective testing are being conducted to validate the satisfactory performance of our IDMS solution and to provide insights about the users' tolerance on asynchrony levels in different IDMS scenarios.
{"title":"Design, development and evaluation of an adaptive and standardized RTP/RTCP-based IDMS solution","authors":"M. Montagud","doi":"10.1145/2502081.2502219","DOIUrl":"https://doi.org/10.1145/2502081.2502219","url":null,"abstract":"Inter-Destination Media Synchronization (IDMS) is essential for enabling pleasant shared media experiences. The goal of my PhD thesis is to design, develop and evaluate an advanced RTP/RTCP-based IDMS solution fitting the requirements of the emerging distributed media consumption paradigm. In particular, standard compliant extensions to RTCP are being specified to allow for an accurate, adaptive and dynamic IDMS control when using RTP for streaming media. Moreover, the feasibility and suitability of several architectural schemes for exchanging the IDMS information, algorithms for allowing a dynamic IDMS monitoring and control, as well as adjustment techniques are being investigated. Objective and subjective testing are being conducted to validate the satisfactory performance of our IDMS solution and to provide insights about the users' tolerance on asynchrony levels in different IDMS scenarios.","PeriodicalId":20448,"journal":{"name":"Proceedings of the 21st ACM international conference on Multimedia","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82815497","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}
One of the most crucial techniques associated with Computer Vision is technology that deals with facial recognition, especially, the automatic estimation of facial expressions. However, in real-time facial expression recognition, when a face turns sideways, the expressional feature extraction becomes difficult as the view of camera changes and recognition accuracy degrades significantly. Therefore, quite many conventional methods are proposed, which are based on static images or limited to situations in which the face is viewed from the front. In this paper, a method that uses Look-Up-Table (LUT) AdaBoost combining with the three-dimensional average face is proposed to solve the problem mentioned above. In order to evaluate the proposed method, the experiment compared with the conventional method was executed. These approaches show promising results and very good success rates. This paper covers several methods that can improve results by making the system more robust.
{"title":"Robust facial expressions recognition using 3D average face and ameliorated adaboost","authors":"Jinhui Chen, Y. Ariki, T. Takiguchi","doi":"10.1145/2502081.2502173","DOIUrl":"https://doi.org/10.1145/2502081.2502173","url":null,"abstract":"One of the most crucial techniques associated with Computer Vision is technology that deals with facial recognition, especially, the automatic estimation of facial expressions. However, in real-time facial expression recognition, when a face turns sideways, the expressional feature extraction becomes difficult as the view of camera changes and recognition accuracy degrades significantly. Therefore, quite many conventional methods are proposed, which are based on static images or limited to situations in which the face is viewed from the front. In this paper, a method that uses Look-Up-Table (LUT) AdaBoost combining with the three-dimensional average face is proposed to solve the problem mentioned above. In order to evaluate the proposed method, the experiment compared with the conventional method was executed. These approaches show promising results and very good success rates. This paper covers several methods that can improve results by making the system more robust.","PeriodicalId":20448,"journal":{"name":"Proceedings of the 21st ACM international conference on Multimedia","volume":"236 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89050805","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}
H. Goëau, P. Bonnet, A. Joly, V. Bakic, Julien Barbe, Itheri Yahiaoui, Souheil Selmi, Jennifer Carré, D. Barthélémy, N. Boujemaa, J. Molino, Grégoire Duché, Aurélien Péronnet
Pl@ntNet is an image sharing and retrieval application for the identification of plants, available on iPhone and iPad devices. Contrary to previous content-based identification applications it can work with several parts of the plant including flowers, leaves, fruits and bark. It also allows integrating user's observations in the database thanks to a collaborative workflow involving the members of a social network specialized on plants. Data collected so far makes it one of the largest mobile plant identification tool.
{"title":"Pl@ntNet mobile app","authors":"H. Goëau, P. Bonnet, A. Joly, V. Bakic, Julien Barbe, Itheri Yahiaoui, Souheil Selmi, Jennifer Carré, D. Barthélémy, N. Boujemaa, J. Molino, Grégoire Duché, Aurélien Péronnet","doi":"10.1145/2502081.2502251","DOIUrl":"https://doi.org/10.1145/2502081.2502251","url":null,"abstract":"Pl@ntNet is an image sharing and retrieval application for the identification of plants, available on iPhone and iPad devices. Contrary to previous content-based identification applications it can work with several parts of the plant including flowers, leaves, fruits and bark. It also allows integrating user's observations in the database thanks to a collaborative workflow involving the members of a social network specialized on plants. Data collected so far makes it one of the largest mobile plant identification tool.","PeriodicalId":20448,"journal":{"name":"Proceedings of the 21st ACM international conference on Multimedia","volume":"1994 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89094397","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}
Ranking on image search has attracted considerable attentions. Many graph-based algorithms have been proposed to solve this problem. Despite their remarkable success, these approaches are restricted to their separated image networks. To improve the ranking performance, one effective strategy is to work beyond the separated image graph by leveraging fruitful information from manual semantic labeling (i.e., tags) associated with images, which leads to the technique of co-ranking images and tags, a representative method that aims to explore the reinforcing relationship between image and tag graphs. The idea of co-ranking is implemented by adopting the paradigm of random walks. However, there are two problems hidden in co-ranking remained to be open: the high computational complexity and the problem of out-of-sample. To address the challenges above, in this paper, we cast the co-ranking process into a Bregman divergence optimization framework under which we transform the original random walk into an equivalent optimal kernel matrix learning problem. Enhanced by this new formulation, we derive a novel extension to achieve a better performance for both in-sample and out-of-sample cases. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of our approach.
{"title":"Efficient image and tag co-ranking: a bregman divergence optimization method","authors":"Lin Wu, Yang Wang, J. Shepherd","doi":"10.1145/2502081.2502156","DOIUrl":"https://doi.org/10.1145/2502081.2502156","url":null,"abstract":"Ranking on image search has attracted considerable attentions. Many graph-based algorithms have been proposed to solve this problem. Despite their remarkable success, these approaches are restricted to their separated image networks. To improve the ranking performance, one effective strategy is to work beyond the separated image graph by leveraging fruitful information from manual semantic labeling (i.e., tags) associated with images, which leads to the technique of co-ranking images and tags, a representative method that aims to explore the reinforcing relationship between image and tag graphs. The idea of co-ranking is implemented by adopting the paradigm of random walks. However, there are two problems hidden in co-ranking remained to be open: the high computational complexity and the problem of out-of-sample. To address the challenges above, in this paper, we cast the co-ranking process into a Bregman divergence optimization framework under which we transform the original random walk into an equivalent optimal kernel matrix learning problem. Enhanced by this new formulation, we derive a novel extension to achieve a better performance for both in-sample and out-of-sample cases. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of our approach.","PeriodicalId":20448,"journal":{"name":"Proceedings of the 21st ACM international conference on Multimedia","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89160786","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}
Most existing video quality metrics measure temporal distortions based on optical-flow estimation, which typically has limited descriptive power of visual dynamics and low efficiency. This paper presents a unified and efficient framework to measure temporal distortions based on a spacetime texture representation of motion. We first propose an effective motion-tuning scheme to capture temporal distortions along motion trajectories by exploiting the distributive characteristic of the spacetime texture. Then we reuse the motion descriptors to build a self-information based spatiotemporal saliency model to guide the spatial pooling. At last, a comprehensive quality metric is developed by combining the temporal distortion measure with spatial distortion measure. Our method demonstrates high efficiency and excellent correlation with the human perception of video quality.
{"title":"Efficient video quality assessment based on spacetime texture representation","authors":"Peng Peng, Kevin J. Cannons, Ze-Nian Li","doi":"10.1145/2502081.2502168","DOIUrl":"https://doi.org/10.1145/2502081.2502168","url":null,"abstract":"Most existing video quality metrics measure temporal distortions based on optical-flow estimation, which typically has limited descriptive power of visual dynamics and low efficiency. This paper presents a unified and efficient framework to measure temporal distortions based on a spacetime texture representation of motion. We first propose an effective motion-tuning scheme to capture temporal distortions along motion trajectories by exploiting the distributive characteristic of the spacetime texture. Then we reuse the motion descriptors to build a self-information based spatiotemporal saliency model to guide the spatial pooling. At last, a comprehensive quality metric is developed by combining the temporal distortion measure with spatial distortion measure. Our method demonstrates high efficiency and excellent correlation with the human perception of video quality.","PeriodicalId":20448,"journal":{"name":"Proceedings of the 21st ACM international conference on Multimedia","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91385930","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}
Previous research has found that the distance metric for similarity estimation is determined by the underlying data noise distribution. The well known Euclidean(L2) and Manhattan (L1) metrics are then justified when the additive noise are Gaussian and Exponential, respectively. However, finding a suitable distance metric for local features is still a challenge when the underlying noise distribution is unknown and could be neither Gaussian nor Exponential. To address this issue, we introduce a modeling framework for arbitrary noise distributions and propose a generalized distance metric for local features based on this framework. We prove that the proposed distance is equivalent to the L1 or the L2 distance when the noise is Gaussian or Exponential. Furthermore, we justify the Hamming metric when the noise meets the given conditions. In that case, the proposed distance is a linear mapping of the Hamming distance. The proposed metric has been extensively tested on a benchmark data set with five state-of-the-art local features: SIFT, SURF, BRIEF, ORB and BRISK. Experiments show that our framework better models the real noise distributions and that more robust results can be obtained by using the proposed distance metric.
{"title":"What are the distance metrics for local features?","authors":"Zhendong Mao, Yongdong Zhang, Q. Tian","doi":"10.1145/2502081.2502134","DOIUrl":"https://doi.org/10.1145/2502081.2502134","url":null,"abstract":"Previous research has found that the distance metric for similarity estimation is determined by the underlying data noise distribution. The well known Euclidean(L2) and Manhattan (L1) metrics are then justified when the additive noise are Gaussian and Exponential, respectively. However, finding a suitable distance metric for local features is still a challenge when the underlying noise distribution is unknown and could be neither Gaussian nor Exponential. To address this issue, we introduce a modeling framework for arbitrary noise distributions and propose a generalized distance metric for local features based on this framework. We prove that the proposed distance is equivalent to the L1 or the L2 distance when the noise is Gaussian or Exponential. Furthermore, we justify the Hamming metric when the noise meets the given conditions. In that case, the proposed distance is a linear mapping of the Hamming distance. The proposed metric has been extensively tested on a benchmark data set with five state-of-the-art local features: SIFT, SURF, BRIEF, ORB and BRISK. Experiments show that our framework better models the real noise distributions and that more robust results can be obtained by using the proposed distance metric.","PeriodicalId":20448,"journal":{"name":"Proceedings of the 21st ACM international conference on Multimedia","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87439925","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}
A peer-to-peer non-linear media streaming system needs to schedule both on-demand and prefetch requests carefully so as to reduce the server load and ensure good user experience. In this work, we propose, Joserlin, a joint request and service scheduling solution that not only alleviates request contentions (requests compete for limited service capacity), but also schedules the prefetch requests by considering their contributions to potential reduction of server load. In particular, we propose a novel request binning algorithm to prevent self-contention among on-demand requests issued from the same peer. A service and rejection policy is devised to resolve contention among on-demand requests issued from different neighbors. More importantly, Joserlin employs a gain function to prioritize prefetch requests at both requesters and responders, and a prefetch request issuing algorithm to fully utilize available upload bandwidth. Evaluation with traces collected from a popular networked virtual environment shows that Joserlin leads to 20%~60% reduction in server load.
{"title":"Joserlin: joint request and service scheduling for peer-to-peer non-linear media access","authors":"Z. Zhao, Wei Tsang Ooi","doi":"10.1145/2502081.2502090","DOIUrl":"https://doi.org/10.1145/2502081.2502090","url":null,"abstract":"A peer-to-peer non-linear media streaming system needs to schedule both on-demand and prefetch requests carefully so as to reduce the server load and ensure good user experience. In this work, we propose, Joserlin, a joint request and service scheduling solution that not only alleviates request contentions (requests compete for limited service capacity), but also schedules the prefetch requests by considering their contributions to potential reduction of server load. In particular, we propose a novel request binning algorithm to prevent self-contention among on-demand requests issued from the same peer. A service and rejection policy is devised to resolve contention among on-demand requests issued from different neighbors. More importantly, Joserlin employs a gain function to prioritize prefetch requests at both requesters and responders, and a prefetch request issuing algorithm to fully utilize available upload bandwidth. Evaluation with traces collected from a popular networked virtual environment shows that Joserlin leads to 20%~60% reduction in server load.","PeriodicalId":20448,"journal":{"name":"Proceedings of the 21st ACM international conference on Multimedia","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77314978","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}
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