A personalized emotion space is proposed to bridge the ＂affective gap＂ in video affective content understanding. In order to unify the discrete and dimensional emotion model, fuzzy C-mean （FCM） clustering algorithm is adopted to divide the emotion space. Gaussian mixture model （GMM） is used to determine the membership functions of typical affective subspaces. At every step of modeling the space, the inputs rely completely on the affective experiences recorded by the audiences. The advantages of the improved V-A （Velance-Arousal） emotion model are the per- sonalization, the ability to define typical affective state areas in the V-A emotion space, and the convenience to explicitly express the intensity of each affective state. The experimental results validate the model and show it can be used as a personalized emotion space for video affective content representation.
SUN Kai,YU Junqing,HUANG Yue,HU Xiaoqiang,LIU Qing College of Computer Science and Technology,Huazhong University of Science and Technology,Wuhan 430074,Hubei,China
在基于样例的视频检索中,视频数据采用多个高维特征数据描述,针对不同的检索应用中这些特征数据的权值经常会发生变化的情况,提出了一种面向可变权值的多特征索引树(multi-feature index tree)结构,以满足用户在样例检索过程中对特征权值进行自定义的设置。多特征索引树采用适应于浏览的树型结构对视频的多个特征向量进行索引,检索时,通过遍历最低一层的集合节点,以减少数据维数对检索效率的影响,并针对多特征索引树结构,提出了一种快速确定检索距离值的ADD-kNN检索算法。实验表明,这种索引结构及相应的检索算法具有较好的性能。
