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      Constructing Distributed Hippocratic Video Databases for Privacy-Preserving Online Patient Training and Counseling

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          Abstract

          Digital video now plays an important role in supporting more profitable online patient training and counseling, and integration of patient training videos from multiple competitive organizations in the health care network will result in better offerings for patients. However, privacy concerns often prevent multiple competitive organizations from sharing and integrating their patient training videos. In addition, patients with infectious or chronic diseases may not want the online patient training organizations to identify who they are or even which video clips they are interested in. Thus, there is an urgent need to develop more effective techniques to protect both video content privacy and access privacy. In this paper, we have developed a new approach to construct a distributed Hippocratic video database system for supporting more profitable online patient training and counseling. First, a new database modeling approach is developed to support concept-oriented video database organization and assign a degree of privacy of the video content for each database level automatically. Second, a new algorithm is developed to protect the video content privacy at the level of individual video clip by filtering out the privacy-sensitive human objects automatically. In order to integrate the patient training videos from multiple competitive organizations for constructing a centralized video database indexing structure, a privacy-preserving video sharing scheme is developed to support privacy-preserving distributed classifier training and prevent the statistical inferences from the videos that are shared for cross-validation of video classifiers. Our experiments on large-scale video databases have also provided very convincing results.

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          Most cited references33

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          Large-Scale Concept Ontology for Multimedia

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            Automatic image segmentation by integrating color-edge extraction and seeded region growing.

            We propose a new automatic image segmentation method. Color edges in an image are first obtained automatically by combining an improved isotropic edge detector and a fast entropic thresholding technique. After the obtained color edges have provided the major geometric structures in an image, the centroids between these adjacent edge regions are taken as the initial seeds for seeded region growing (SRG). These seeds are then replaced by the centroids of the generated homogeneous image regions by incorporating the required additional pixels step by step. Moreover, the results of color-edge extraction and SRG are integrated to provide homogeneous image regions with accurate and closed boundaries. We also discuss the application of our image segmentation method to automatic face detection. Furthermore, semantic human objects are generated by a seeded region aggregation procedure which takes the detected faces as object seeds.
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              What size test set gives good error rate estimates?

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                Author and article information

                Contributors
                Journal
                IEEE Trans Inf Technol Biomed
                IEEE Trans Inf Technol Biomed
                0047700
                ITIBFX
                TITB
                Ieee Transactions on Information Technology in Biomedicine
                IEEE
                1089-7771
                1558-0032
                July 2010
                01 September 2009
                : 14
                : 4
                : 1014-1026
                Affiliations
                [1 ] institutionSchool of Electronics and Information, institutionNorthwestern Polytechnical University; Xi'an 710072 China
                [2 ] departmentDepartment of Information and Communication Technologies, institutionGraduate School of Engineering, institutionOsaka University; Osaka 565-0871 Japan
                [3 ] institutionSoftware Engineering Institute, institutionEast China Normal University; Shanghai 200062 China
                [4 ] institutionHewlett-Packard Laboratories; Palo AltoCA 94304 USA
                [5 ] departmentDepartment of Computer Science, institutionUniversity of North Carolina; Charlotte NC 28223 USA
                Article
                10.1109/TITB.2009.2029695
                7176470
                19726265
                ee14b478-05d7-4817-a83a-4e217a674b9d
                Copyright @ 2009

                This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

                History
                : 12 February 2009
                : 17 May 2009
                : 31 July 2009
                : 09 July 2010
                Page count
                Figures: 17, Tables: 1, Equations: 276, References: 39, Pages: 13
                Funding
                Funded by: National Science Foundation;
                Award ID: 0601542-IIS
                Award ID: 0208539-IIS
                Funded by: Grant-in-Aid for scientific research from the Japan Society for the Promotion of Science;
                Funded by: Shanghai Pujiang Program;
                Award ID: 08PJ1404600
                Funded by: National Science Foundation of China;
                Award ID: 60803077
                Funded by: Program for New Century Excellent Talents in University;
                Award ID: NCET-07-0693
                Funded by: National Science Foundation of China;
                Award ID: 60875016
                Funded by: Program for New Century Excellent Talents in University;
                Award ID: NCET-10-0071
                This work was supported in part by the National Science Foundation under Grant 0601542-IIS and Grant 0208539-IIS, and by a Grant-in-Aid for scientific research from the Japan Society for the Promotion of Science. The work of H. Luo was supported by Shanghai Pujiang Program under Grant 08PJ1404600 and National Science Foundation of China under Grant 60803077. The work of J. Peng was supported by the Program for New Century Excellent Talents in University under Grant NCET-07-0693 and by the National Science Foundation of China under Grant 60875016. The work of J. Fan was also supported by the Program for New Century Excellent Talents in University under Grant NCET-10-0071.
                Categories
                Regular Papers

                online patient training and counseling,privacy-preserving classifier training and validation,privacy-preserving video database indexing,privacy-preserving video sharing

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