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1. 发明申请

US20130101209A1 METHOD AND SYSTEM FOR EXTRACTION AND ASSOCIATION OF OBJECT OF INTEREST IN VIDEO 审中-公开
标题翻译：在视频中提取和关联对象的方法和系统
公开(公告)号：US20130101209A1
公开(公告)日：2013-04-25
申请号：US13715632
申请日：2012-12-14
申请人： Huawei Technologies Co., Ltd. , Peking University
发明人： Yonghong Tian , Haonan Yu , Jia Li , Yunchao Gao , Jun Zhang , Jun Yan
IPC分类号： G06K9/80
CPC分类号： G06K9/80 , G06F16/78 , G06K9/3241 , G06K2009/366 , H04N21/44008
摘要： The present disclosure relates to an image and video processing method, and in particular, to a two-phase-interaction-based extraction and association method for an object of interest in a video. In the method, a user performs coarse positioning interaction by an interactive method which is not limited to a normal manner and has a low requirement for prior knowledge; based on this, a certain extraction algorithm which is fast and easy to implement is adopted to perform multi-parameter extraction on the object of interest. In the method, on the basis of mining video information fully and ensuring user preference, in a manner where the viewing of the user is not affected, associate value-added information with the object which the user is interested in, thereby meeting the user's requirement for deeply knowing and further exploring an attention area.
摘要翻译：本公开涉及一种图像和视频处理方法，特别地涉及一种用于视频中的感兴趣对象的基于两相交互的提取和关联方法。在该方法中，用户通过交互方式执行粗略的定位交互，该交互方法不限于正常方式，对于先验知识的要求较低; 基于此，采用快速，易于实现的某种提取算法对感兴趣的对象进行多参数提取。在该方法中，基于完全挖掘视频信息并确保用户偏好，以不影响用户观看的方式，将增值信息与用户感兴趣的对象相关联，从而满足用户的要求深入了解进一步探索关注领域。

2. 发明授权

US08750602B2 Method and system for personalized advertisement push based on user interest learning 有权
标题翻译：基于用户兴趣学习的个性化广告推送方法与系统
公开(公告)号：US08750602B2
公开(公告)日：2014-06-10
申请号：US13709795
申请日：2012-12-10
申请人： Huawei Technologies Co., Ltd.
发明人： Jia Li , Yunchao Gao , Haonan Yu , Jun Zhang , Yonghong Tian , Jun Yan
IPC分类号： G06K9/62
CPC分类号： G06K9/46 , G06F17/3079 , G06K9/628 , G06Q30/00
摘要： Embodiments of the present invention relate to a method and a system for personalized advertisement push based on user interest learning. The method may include: obtaining multiple user interest models through multitask sorting learning; extracting an object of interest in a video according to the user interest models; and extracting multiple visual features of the object of interest, and according to the visual features, retrieving related advertising information in an advertisement database. Through the method and the system provided in embodiments of the present invention, a push advertisement may be closely relevant to the content of the video, thereby meeting personalized requirements of a user to a certain extent and achieving personalized advertisement push.
摘要翻译：本发明的实施例涉及一种基于用户兴趣学习的个性化广告推送的方法和系统。该方法可以包括：通过多任务排序学习获得多个用户兴趣模型; 根据用户兴趣模型提取视频中的感兴趣对象; 并且提取所述感兴趣对象的多个视觉特征，并且根据所述视觉特征，在广告数据库中检索相关广告信息。通过本发明实施例提供的方法和系统，推送广告可以与视频的内容密切相关，从而在一定程度上满足用户的个性化需求并实现个性化广告推送。

3. 发明申请

US20130094756A1 METHOD AND SYSTEM FOR PERSONALIZED ADVERTISEMENT PUSH BASED ON USER INTEREST LEARNING 有权
标题翻译：基于用户兴趣的个性化广告推送方法与系统
公开(公告)号：US20130094756A1
公开(公告)日：2013-04-18
申请号：US13709795
申请日：2012-12-10
申请人： Huawei Technologies Co., Ltd.
发明人： Jia Li , Yunchao Gao , Haonan Yu , Jun Zhang , Yonghong Tian , Jun Yan
IPC分类号： G06K9/46
CPC分类号： G06K9/46 , G06F17/3079 , G06K9/628 , G06Q30/00
摘要： Embodiments of the present invention relate to a method and a system for personalized advertisement push based on user interest learning. The method may include: obtaining multiple user interest models through multitask sorting learning; extracting an object of interest in a video according to the user interest models; and extracting multiple visual features of the object of interest, and according to the visual features, retrieving related advertising information in an advertisement database. Through the method and the system provided in embodiments of the present invention, a push advertisement may be closely relevant to the content of the video, thereby meeting personalized requirements of a user to a certain extent and achieving personalized advertisement push.
摘要翻译：本发明的实施例涉及一种基于用户兴趣学习的个性化广告推送的方法和系统。该方法可以包括：通过多任务排序学习获得多个用户兴趣模型; 根据用户兴趣模型提取视频中的感兴趣对象; 并且提取所述感兴趣对象的多个视觉特征，并且根据所述视觉特征，在广告数据库中检索相关广告信息。通过本发明实施例提供的方法和系统，推送广告可以与视频的内容密切相关，从而在一定程度上满足用户的个性化需求并实现个性化广告推送。

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