一种基于反馈模糊图论的视频多语义标注算法

Video Multi-semantic Annotation Algorithm Based on Feedback Fuzzy Graph Theory

为了弥补视频语义检索中视频底层特征与高层语义概念之间的"语义鸿沟",提出了一种基于反馈模糊图论的视频多语义标注算法。该算法首先构造一个包括所有数据的时间和空间分布信息的小样本集,据此进行人工标注并将其作为训练集。然后将模糊算子引入图论中,将语义概念间的关系模糊化,以实现模糊推理。最后将标注完成的测试集中的样本加入到训练集中,以完成视频标注的反馈。实验结果表明,使用反馈的模糊图不仅可以很好地建立语义概念间的关系,还能提高视频标注的准确率,表现出良好的性能。

For bridging semantic gap between video low-level features and high-level semantic concepts in the semantic- based video retrieval system, the video multi-semantic annotation algorithm based on feedback fuzzy graph theory was proposed. First, a training set which includes most temporal and spatial distribution of the whole data is made up and it will achieve a satisfying performance even in the case of limited size of training set. Secondly, the fuzzy operators are ap- plied to graph theory to achieve fuzzy reasoning by using fuzzy semantic. Last, in order to finish the feedback of video annotation, some temples from the testing set that have finished annotation are selected and added into the training set. Experimental results indicate that feedback fuzzy graph not only sets up the relationship between semantic concepts well, but also improves the precision of annotation and shows good performance.