Abstract:
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.
Abstract:
A microcell creating method based on macrocell network coverage and a base station are disclosed. The method may include setting a beam width and a beam direction of a highly directional antenna according to location information of a hotspot area, and generating microcell coverage or the hotspot area by using beams generated by the highly directional antenna. Embodiments of the present invention may keep the micro base station location unchanged when the hotspot area changes, and may implement microcell coverage for the hotspot area simply by adjusting the beam width and the beam direction of the highly directional antenna.
Abstract:
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.
Abstract:
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.