This paper introduces a distributed approach for playback of video content at resolutions of 4K (digital cinema) and well beyond. This approach is designed for scalable, high-resolution, multi-tile display environments, which are controlled by a cluster of machines, with each node driving one or multiple displays. A preparatory tiling pass separates the original video into a user definable n-by-m array of equally sized video tiles, each of which is individually compressed. By only reading and rendering the video tiles that correspond to a given node's viewpoint, the computation power required for video playback can be distributed over multiple machines, resulting in a highly scalable video playback system. This approach exploits the computational parallelism of the display cluster while only using minimal network resources in order to maintain software-level synchronization of the video playback. While network constraints limit the maximum resolution of other high-resolution video playback approaches, this algorithm is able to scale to video at resolutions of tens of millions of pixels and beyond. Furthermore the system allows for flexible control of the video characteristics, allowing content to be interactively reorganized while maintaining smooth playback. This approach scales well for concurrent playback of multiple videos and does not require any specialized video decoding hardware to achieve ultra-high resolution video playback.
{"title":"RSVP: Ridiculously Scalable Video Playback on Clustered Tiled Displays","authors":"J. Kimball, K. Ponto, T. Wypych, F. Kuester","doi":"10.1109/ISM.2013.12","DOIUrl":"https://doi.org/10.1109/ISM.2013.12","url":null,"abstract":"This paper introduces a distributed approach for playback of video content at resolutions of 4K (digital cinema) and well beyond. This approach is designed for scalable, high-resolution, multi-tile display environments, which are controlled by a cluster of machines, with each node driving one or multiple displays. A preparatory tiling pass separates the original video into a user definable n-by-m array of equally sized video tiles, each of which is individually compressed. By only reading and rendering the video tiles that correspond to a given node's viewpoint, the computation power required for video playback can be distributed over multiple machines, resulting in a highly scalable video playback system. This approach exploits the computational parallelism of the display cluster while only using minimal network resources in order to maintain software-level synchronization of the video playback. While network constraints limit the maximum resolution of other high-resolution video playback approaches, this algorithm is able to scale to video at resolutions of tens of millions of pixels and beyond. Furthermore the system allows for flexible control of the video characteristics, allowing content to be interactively reorganized while maintaining smooth playback. This approach scales well for concurrent playback of multiple videos and does not require any specialized video decoding hardware to achieve ultra-high resolution video playback.","PeriodicalId":6311,"journal":{"name":"2013 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)","volume":"50 1","pages":"9-16"},"PeriodicalIF":0.0,"publicationDate":"2013-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90226581","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 presents action recognition using effective mask patterns selected from an classificational viewpoint. Cubic higher-order local auto-correlation (CHLAC) feature is robust to position changes of human actions in a video, and its effectiveness for action recognition was already shown. However, the mask patterns for extracting cubic higher-order local auto-correlation (CHLAC) features are fixed. In other words, the mask patterns are independent of action classes, and the features extracted from those mask patterns are not specialized for each action. Thus, we propose automatic creation of specialized mask patterns for each action. Our approach consists of 2 steps. First, mask patterns are created by clustering of local spatio-temporal regions in each action. However, unnecessary mask patterns such as same patterns and mask patterns with all 0 or 1 are included. Then we select the effective mask patterns for classification by feature selection techniques. Through experiments using the KTH dataset, the effectiveness of our method is shown.
{"title":"Action Recognition Using Effective Mask Patterns Selected from a Classificational Viewpoint","authors":"Takumi Hayashi, K. Hotta","doi":"10.1109/ISM.2013.31","DOIUrl":"https://doi.org/10.1109/ISM.2013.31","url":null,"abstract":"This paper presents action recognition using effective mask patterns selected from an classificational viewpoint. Cubic higher-order local auto-correlation (CHLAC) feature is robust to position changes of human actions in a video, and its effectiveness for action recognition was already shown. However, the mask patterns for extracting cubic higher-order local auto-correlation (CHLAC) features are fixed. In other words, the mask patterns are independent of action classes, and the features extracted from those mask patterns are not specialized for each action. Thus, we propose automatic creation of specialized mask patterns for each action. Our approach consists of 2 steps. First, mask patterns are created by clustering of local spatio-temporal regions in each action. However, unnecessary mask patterns such as same patterns and mask patterns with all 0 or 1 are included. Then we select the effective mask patterns for classification by feature selection techniques. Through experiments using the KTH dataset, the effectiveness of our method is shown.","PeriodicalId":6311,"journal":{"name":"2013 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)","volume":"67 1","pages":"140-146"},"PeriodicalIF":0.0,"publicationDate":"2013-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84408069","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 proposes a method of finding the relationship between objects based on their spatial arrangement in a set of tagged images. Based on the relative coordinates of each object tag, we compute a joint Relativity between each tag pair. We then propose an efficient image search method using the joint Relativity graphs and provide simple examples where the proposed Relational Social Image (RSI) search produces more relevant and intuitive results than simple search.
{"title":"Relational Social Image Search","authors":"P. Aarabi","doi":"10.1109/ISM.2013.105","DOIUrl":"https://doi.org/10.1109/ISM.2013.105","url":null,"abstract":"This paper proposes a method of finding the relationship between objects based on their spatial arrangement in a set of tagged images. Based on the relative coordinates of each object tag, we compute a joint Relativity between each tag pair. We then propose an efficient image search method using the joint Relativity graphs and provide simple examples where the proposed Relational Social Image (RSI) search produces more relevant and intuitive results than simple search.","PeriodicalId":6311,"journal":{"name":"2013 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)","volume":"401 1","pages":"520-521"},"PeriodicalIF":0.0,"publicationDate":"2013-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84849976","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}
In current biological image analysis, the temporal stage information, such as the developmental stage in the Drosophila development in situ hybridization images, is important for biological knowledge discovery. Such information is usually gained through visual inspection by experts. However, as the high-throughput imaging technology becomes increasingly popular, the demand for labor effort on annotating, labeling, and organizing the images for efficient image retrieval has increased tremendously, making manual data processing infeasible. In this paper, a novel multi-layer classification framework is proposed to discover the temporal information of the biological images automatically. Rather than solving the problem directly, the proposed framework uses the idea of ``divide and conquer'' to create some middle level classes, which are relatively easy to annotate, and to train the proposed subspace-based classifiers on the subsets of data belonging to these categories. Next, the results from these classifiers are integrated to improve the final classification performance. In order to appropriately integrate the outputs from different classifiers, a multi-class based closed form quadratic cost function is defined as the optimization target and the parameters are estimated using the gradient descent algorithm. Our proposed framework is tested on three biological image data sets and compared with other state-of-the-art algorithms. The experimental results demonstrate that the proposed middle-level classes and the proper integration of the results from the corresponding classifiers are promising for mining the temporal stage information of the biological images.
{"title":"Biological Image Temporal Stage Classification via Multi-layer Model Collaboration","authors":"Tao Meng, M. Shyu","doi":"10.1109/ISM.2013.15","DOIUrl":"https://doi.org/10.1109/ISM.2013.15","url":null,"abstract":"In current biological image analysis, the temporal stage information, such as the developmental stage in the Drosophila development in situ hybridization images, is important for biological knowledge discovery. Such information is usually gained through visual inspection by experts. However, as the high-throughput imaging technology becomes increasingly popular, the demand for labor effort on annotating, labeling, and organizing the images for efficient image retrieval has increased tremendously, making manual data processing infeasible. In this paper, a novel multi-layer classification framework is proposed to discover the temporal information of the biological images automatically. Rather than solving the problem directly, the proposed framework uses the idea of ``divide and conquer'' to create some middle level classes, which are relatively easy to annotate, and to train the proposed subspace-based classifiers on the subsets of data belonging to these categories. Next, the results from these classifiers are integrated to improve the final classification performance. In order to appropriately integrate the outputs from different classifiers, a multi-class based closed form quadratic cost function is defined as the optimization target and the parameters are estimated using the gradient descent algorithm. Our proposed framework is tested on three biological image data sets and compared with other state-of-the-art algorithms. The experimental results demonstrate that the proposed middle-level classes and the proper integration of the results from the corresponding classifiers are promising for mining the temporal stage information of the biological images.","PeriodicalId":6311,"journal":{"name":"2013 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)","volume":"14 1","pages":"30-37"},"PeriodicalIF":0.0,"publicationDate":"2013-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85048678","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}
Scene flow is the motion of the 3D world, it is used in obstacle avoidance, slow motion interpolation, surveillance, studying human behavior, and much more. A mobile implementation of scene flow can greatly increase the flexibility of scene flow applications. Furthermore, combining multiple scene flows to one panoramic scene flow can aid in these tasks: allowing coverage of dead spots in surveillance, studying human motion from multiple views, or simply obtain a larger motion view of a scene. In this paper, a robust algorithm for building panoramic scene flow obtained from a mobile device is described. Since scene flow is estimated from a mobile device, observer motion is compensated for using least squares fitting over the entire scene. Furthermore, noise is reduced and outliers are eliminated from the 3D motion field using motion model fitting. The results demonstrate the effectiveness of the suggested algorithm for constructing a scene flow panorama from moving sources.
{"title":"Mobile Scene Flow Synthesis","authors":"V. Ly, C. Kambhamettu","doi":"10.1109/ISM.2013.85","DOIUrl":"https://doi.org/10.1109/ISM.2013.85","url":null,"abstract":"Scene flow is the motion of the 3D world, it is used in obstacle avoidance, slow motion interpolation, surveillance, studying human behavior, and much more. A mobile implementation of scene flow can greatly increase the flexibility of scene flow applications. Furthermore, combining multiple scene flows to one panoramic scene flow can aid in these tasks: allowing coverage of dead spots in surveillance, studying human motion from multiple views, or simply obtain a larger motion view of a scene. In this paper, a robust algorithm for building panoramic scene flow obtained from a mobile device is described. Since scene flow is estimated from a mobile device, observer motion is compensated for using least squares fitting over the entire scene. Furthermore, noise is reduced and outliers are eliminated from the 3D motion field using motion model fitting. The results demonstrate the effectiveness of the suggested algorithm for constructing a scene flow panorama from moving sources.","PeriodicalId":6311,"journal":{"name":"2013 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)","volume":"6 1","pages":"439-444"},"PeriodicalIF":0.0,"publicationDate":"2013-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82391933","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}
In this demo paper, we present an image browsing system that is suitable for online visualisation and browsing of search results from Google Images. Our approach is based on the Huffman tables available in the JPEG headers of Google Images thumbnails. Since these are adapted to the images, we employ them directly as image features. We then generate a visualisation of the search results by projection onto a 2-dimensional visualisation space based on principal component analysis derived from the Huffman entries. Images are dynamically placed into a grid structure and organised in a tree-like hierarchy for visual browsing. Since we utilise information only from the JPEG header, the requirements in terms of bandwidth are low, while no explicit feature calculation needs to be performed, thus allowing for interactive browsing of online image search results.
{"title":"Similarity-Based Browsing of Image Search Results","authors":"David Edmundson, G. Schaefer, M. E. Celebi","doi":"10.1109/ISM.2013.97","DOIUrl":"https://doi.org/10.1109/ISM.2013.97","url":null,"abstract":"In this demo paper, we present an image browsing system that is suitable for online visualisation and browsing of search results from Google Images. Our approach is based on the Huffman tables available in the JPEG headers of Google Images thumbnails. Since these are adapted to the images, we employ them directly as image features. We then generate a visualisation of the search results by projection onto a 2-dimensional visualisation space based on principal component analysis derived from the Huffman entries. Images are dynamically placed into a grid structure and organised in a tree-like hierarchy for visual browsing. Since we utilise information only from the JPEG header, the requirements in terms of bandwidth are low, while no explicit feature calculation needs to be performed, thus allowing for interactive browsing of online image search results.","PeriodicalId":6311,"journal":{"name":"2013 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)","volume":"194 1","pages":"502-503"},"PeriodicalIF":0.0,"publicationDate":"2013-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73195898","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}
As technology for data storage and computing enhances on a new dimension, real world evidence based decision has become possible, and a new concept called "Big Data" has emerged. Leaders for Big Data technology development are mainly national security and medical field due to their urgent need of decision based on big data. Recent revelation of collection and analysis activities of National Security Agency (NSA) that cover massive data corresponding to the area of people's privacy triggered much controversy about concerns for infringing privacy and violation of constitutional rights. This, in turn, became opportunity of producing various analysis and opinions about the current level of big data technology, future possibility, types of work that big data has enabled, and technological and legal means for preventing expected abuse. The purposes of this paper are surveying materials about legal issues regarding NSA's activity with big data, and organizing feedbacks collected from various sources of the society.
{"title":"Big Data and NSA Surveillance -- Survey of Technology and Legal Issues","authors":"Chanmin Park, Taehyung Wang","doi":"10.1109/ISM.2013.103","DOIUrl":"https://doi.org/10.1109/ISM.2013.103","url":null,"abstract":"As technology for data storage and computing enhances on a new dimension, real world evidence based decision has become possible, and a new concept called \"Big Data\" has emerged. Leaders for Big Data technology development are mainly national security and medical field due to their urgent need of decision based on big data. Recent revelation of collection and analysis activities of National Security Agency (NSA) that cover massive data corresponding to the area of people's privacy triggered much controversy about concerns for infringing privacy and violation of constitutional rights. This, in turn, became opportunity of producing various analysis and opinions about the current level of big data technology, future possibility, types of work that big data has enabled, and technological and legal means for preventing expected abuse. The purposes of this paper are surveying materials about legal issues regarding NSA's activity with big data, and organizing feedbacks collected from various sources of the society.","PeriodicalId":6311,"journal":{"name":"2013 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)","volume":"15 1","pages":"516-517"},"PeriodicalIF":0.0,"publicationDate":"2013-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80696047","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}
Automatic human action recognition is a core functionality of systems for video surveillance and human-object interaction. Conventional vision-based systems for human action recognition require the use of segmentation in order to achieve an acceptable level of recognition effectiveness. However, generic techniques for automatic segmentation are currently not available yet. Therefore, in this paper, we propose a novel sparse representation-based method for human action recognition, taking advantage of the observation that, although the location and size of the action region in a test video clip is unknown, the construction of a dictionary can leverage information about the location and size of action regions in training video clips. That way, we are able to segment, implicitly, action and context information in a test video clip, thus improving the effectiveness of classification. That way, we are also able to develop a context-adaptive classification strategy. As shown by comparative experimental results obtained for the UCF Sports Action data set, the proposed method facilitates effective human action recognition, even when testing does not rely on explicit segmentation.
{"title":"Sparse Representation-Based Human Action Recognition Using an Action Region-Aware Dictionary","authors":"Hyun-seok Min, W. D. Neve, Yong Man Ro","doi":"10.1109/ISM.2013.30","DOIUrl":"https://doi.org/10.1109/ISM.2013.30","url":null,"abstract":"Automatic human action recognition is a core functionality of systems for video surveillance and human-object interaction. Conventional vision-based systems for human action recognition require the use of segmentation in order to achieve an acceptable level of recognition effectiveness. However, generic techniques for automatic segmentation are currently not available yet. Therefore, in this paper, we propose a novel sparse representation-based method for human action recognition, taking advantage of the observation that, although the location and size of the action region in a test video clip is unknown, the construction of a dictionary can leverage information about the location and size of action regions in training video clips. That way, we are able to segment, implicitly, action and context information in a test video clip, thus improving the effectiveness of classification. That way, we are also able to develop a context-adaptive classification strategy. As shown by comparative experimental results obtained for the UCF Sports Action data set, the proposed method facilitates effective human action recognition, even when testing does not rely on explicit segmentation.","PeriodicalId":6311,"journal":{"name":"2013 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)","volume":"446 1","pages":"133-139"},"PeriodicalIF":0.0,"publicationDate":"2013-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82900745","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}
Ihab Al Kabary, Ivan Giangreco, H. Schuldt, Fabrice Matulic, M. Norrie
The enormous increase of digital image collections urgently necessitates effective, efficient, and in particular highly flexible approaches to image retrieval. Different search paradigms such as text search, query-by-example, or query-by-sketch need to be seamlessly combined and integrated to support different information needs and to allow users to start (and subsequently refine) queries with any type of object. In this paper, we present QUEST (Query by Example, Sketch and Text), a novel flexible multi-modal content-based image retrieval (CBIR) framework. QUEST seamlessly integrates and blends multiple modes of image retrieval, thereby accumulating the strengths of each individual mode. Moreover, it provides several implementations of the different query modes and allows users to select, combine and even superimpose the mode(s) most appropriate for each search task. The combination of search paradigms is by itself done in a very flexible way: either sequentially, where one query mode starts with the result set of the previous one (i.e., for incrementally refining and/or extending a query) or by supporting different paradigms at the same time (e.g., creating an artificial query image by superimposing a query image with a sketch, thereby directly integrating query-by-example and query-by-sketch). We present the overall architecture of QUEST and the dynamic combination and integration of the query modes it supports. Furthermore, we provide first evaluation results that show the effectiveness and the gain in efficiency that can be achieved with the combination of different search modes in QUEST.
数字图像馆藏的大量增加迫切需要有效、高效、特别是高度灵活的图像检索方法。不同的搜索范式(如文本搜索、按示例查询或按草图查询)需要无缝地组合和集成，以支持不同的信息需求，并允许用户使用任何类型的对象启动(并随后改进)查询。在本文中，我们提出了QUEST (Query by Example, Sketch and Text)，一种新颖灵活的多模态基于内容的图像检索(CBIR)框架。QUEST将多种图像检索模式无缝集成和融合，从而积累了每种模式的优势。此外，它还提供了几种不同查询模式的实现，并允许用户选择、组合甚至叠加最适合每个搜索任务的模式。搜索范式的组合本身是以一种非常灵活的方式完成的:要么是顺序的，其中一个查询模式从前一个查询模式的结果集开始(即，增量地精炼和/或扩展查询)，要么是同时支持不同的范式(例如，通过将查询图像与草图叠加来创建人工查询图像，从而直接集成按示例查询和按草图查询)。我们给出了QUEST的总体架构以及它所支持的查询模式的动态组合和集成。此外，我们提供了第一个评估结果，显示了QUEST中不同搜索模式组合可以实现的有效性和效率增益。
{"title":"QUEST: Towards a Multi-modal CBIR Framework Combining Query-by-Example, Query-by-Sketch, and Text Search","authors":"Ihab Al Kabary, Ivan Giangreco, H. Schuldt, Fabrice Matulic, M. Norrie","doi":"10.1109/ISM.2013.84","DOIUrl":"https://doi.org/10.1109/ISM.2013.84","url":null,"abstract":"The enormous increase of digital image collections urgently necessitates effective, efficient, and in particular highly flexible approaches to image retrieval. Different search paradigms such as text search, query-by-example, or query-by-sketch need to be seamlessly combined and integrated to support different information needs and to allow users to start (and subsequently refine) queries with any type of object. In this paper, we present QUEST (Query by Example, Sketch and Text), a novel flexible multi-modal content-based image retrieval (CBIR) framework. QUEST seamlessly integrates and blends multiple modes of image retrieval, thereby accumulating the strengths of each individual mode. Moreover, it provides several implementations of the different query modes and allows users to select, combine and even superimpose the mode(s) most appropriate for each search task. The combination of search paradigms is by itself done in a very flexible way: either sequentially, where one query mode starts with the result set of the previous one (i.e., for incrementally refining and/or extending a query) or by supporting different paradigms at the same time (e.g., creating an artificial query image by superimposing a query image with a sketch, thereby directly integrating query-by-example and query-by-sketch). We present the overall architecture of QUEST and the dynamic combination and integration of the query modes it supports. Furthermore, we provide first evaluation results that show the effectiveness and the gain in efficiency that can be achieved with the combination of different search modes in QUEST.","PeriodicalId":6311,"journal":{"name":"2013 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)","volume":"80 1","pages":"433-438"},"PeriodicalIF":0.0,"publicationDate":"2013-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77368689","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}
Mohammed F. Alhamid, Majdi Rawashdeh, Abdulmotaleb El Saddik
Given today's mobile and smart devices, and the ability to access different multimedia contents in real-time, it is difficult for users to find the right multimedia content from such a large number of choices. Users also consume diverse multimedia based on many contexts, with different personal preferences and settings. For these reasons, there is a need to reinforce recommendation process with context-adaptive information that can be used to select the right multimedia content and deliver the recommendations in preferred mechanisms. This paper proposes a framework to establish a bridge between the multimedia content, the user and joint preferences, contextual information including the physiological parameters, and the Ambient Intelligent (AmI) environment, using multi-modal recommendation interfaces.
{"title":"Towards Context-Aware Recommendations of Multimedia in an Ambient Intelligence Environment","authors":"Mohammed F. Alhamid, Majdi Rawashdeh, Abdulmotaleb El Saddik","doi":"10.1109/ISM.2013.80","DOIUrl":"https://doi.org/10.1109/ISM.2013.80","url":null,"abstract":"Given today's mobile and smart devices, and the ability to access different multimedia contents in real-time, it is difficult for users to find the right multimedia content from such a large number of choices. Users also consume diverse multimedia based on many contexts, with different personal preferences and settings. For these reasons, there is a need to reinforce recommendation process with context-adaptive information that can be used to select the right multimedia content and deliver the recommendations in preferred mechanisms. This paper proposes a framework to establish a bridge between the multimedia content, the user and joint preferences, contextual information including the physiological parameters, and the Ambient Intelligent (AmI) environment, using multi-modal recommendation interfaces.","PeriodicalId":6311,"journal":{"name":"2013 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)","volume":"1 1","pages":"409-414"},"PeriodicalIF":0.0,"publicationDate":"2013-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85343348","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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