HCRF和网络文本的精彩事件自动检测定位

Wonderful events automatic detection and location of the HCRF and webcast text

利用隐条件随机场(HCRF)在表达和识别语义事件方面的强大功能,并结合网络直播文本信息,提出了一种新的精彩事件自动检测框架.首先,通过对网络直播文本进行分析处理,获得每种精彩事件对应的关键词组合;其次,对待检测的网络直播文本进行分类,获得每个精彩事件发生的时间标签;然后,构建用于提出的语义镜头标注的HCRF模型,实现多种语义镜头的同时标注,得到视频语义镜头标签序列;最后,结合多模态语义线索,在小规模训练样本的情况下,有效建立了精彩事件检测与定位的HCRF模型.文中基于视频底层特征、多模态语义线索及精彩语义事件之间的映射关系,从结构语义的多个维度挖掘了精彩事件的内在规律,准确实现了精彩事件的自动检测、定位与分割.实验结果证明了该模型的有效性.

Based on the powerful function of the hidden conditional random fields （ HCRF） model in the expression and identification of semantic events and combining the webcast text information , a new framework for wonderful events automatic detection is put forward . Firstly , by analyzing and processing the webcast text , keyword combinations corresponding to each exciting event are obtained . Secondly , by classifying the webcast text to be detected , the happening time labels of each wonderful event are obtained . Thirdly , an HCRF model for semantic shot annotation is built to realize the semantic annotation of multiple types of semantic shots simultaneously , and the semantic shot sequence of the video clip is obtained . Finally , combining the multi‐modal semantic clues , an HCRF model for the wonderful events detection and localization is effectively built in the case of small‐scale training samples . Based on the mapping relationship among video low‐level features , the multi‐modal semantic clues and the wonderful semantic events , the inherent patterns of the wonderful events are excavated deeply in the multiple dimensions of the semantic structure , and then the wonderful events automatic detection , localization and segmentation are precisely achieved . Experiments show the effectiveness of this model .