一种基于语义分析的大数据视频标注方法

提出一种基于Spark计算框架的海量视频语义标注方法。将存储在Hadoop分布式文件系统（Hadoop distributed file system,HDFS）上的海量视频部署到若干计算节点上，依据分形特征实现镜头快速分割。提取样本关键帧的颜色、纹理和分形特征向量，进行元学习策略训练，进而形成视觉词典。根据视觉词典对检测视频内容进行分析，产生一系列能表征视频内容的视觉单词。根据重要程度，通过马尔科夫链按重要程度对视频的视觉单词进行排序，并将排列结果作为该视频的标注。最后，从检测正确率、平均运行时间和扩展效能方面与传统分布式计算模型进行了对比。

Big Data Video Annotation Based on Semantic Analysis

A semantic annotation method is presented for massive videos based on Spark computing model. These massive videos are stored on Hadoop distributed file system (HDFS) and distributed to several nodes. These shot segmentationsin videos are realized by the fractal dimension method, and then the key frames of video shots are extracted based on color features,texture features and fractal features.The features in shots are trained using meta-learning strategy, and changed to video words and collected into the visual video dictionary.So video content is predicted and expressed by several video words according to the video dictionary. Then the video words are arranged according to importance sequence by Markov chains and the important words are described as video content prediction. Compared with the traditional distributed computing model, the Spark computing method illustrates the superiority from the correct rate, the average running time and the expansion efficiency.