一个镜头中的语义概念通常依赖于其他多个语义概念，几个同时出现的语义概念决定着另一个语义概念的出现。为此提出一种概念间关联依赖多标记视频语义概念分类方法。为得到概念间关联依赖规则，合并和修剪技术用于产生候选的项集；计算各候选项集的支持度后，得到满足最小支持度的频繁项集；经过一系列频繁项集迭代，产生具有强关联依赖关系的复合标记；在标记过程中，将具有强关联依赖关系的多个语义标记作为单标记进行标注。实验结果表明，对真实媒体数据本文方法比现有多标记分类方法更能有效进行分类。

In video data， one concept in one shot are usually dependent on others concepts. Several semantic concepts appearing in one time often determine the presence of other concepts. An inter-concepts association and dependency multi-label video semantic concept classification approach is proposed in this paper. In order to generate association and dependency relation between concepts， join and prune phases are used to extract potential itemsets. After calculating the minimum support of each itemset， frequency itemsets meeting the user specified minimum support are selected. In the iteration process of generation frequency itemsets， compound labels with strong association and dependency relation of inter-concepts are obtained. Finally， compound labels are considered as a single label in the annotation step. Experiments on real-world multi-label media data show that this method the methods beat accuracy of existing multi-label learning methods with statistically significant improvements.