基于SVM和ICA的视频帧字幕自动定位与提取

视频字幕蕴涵了丰富语义,可以用来对相应视频流进行高级语义标注,但由于先前视频字幕提取考虑的只是如何尽可能定义好字幕特征,而忽视了分类学习机自身的学习推广能力.针对这一局限性,提出了一种基于支持向量机和独立分量分析的视频帧字幕定位与提取算法.该算法是首先将原始图象帧分割成N×N大小子块,同时将每个子块标注为字幕块和非字幕块两类;然后从每个子块提取能够保持相互高阶独立的独立分量特征去训练支持向量机分类器;最后结合金字塔模型和去噪方法,用训练好的支持向量机来实现对视频字幕区域自动定位提取.由于支持向量机能够在样本不是很多的情况下,具有良好的分类推广能力以及能使独立成分特征之间彼此保持高阶独立性,与其他视频帧字幕定位提取算法比较的结果表明,该算法具有明显的优点.

Automatic Caption Location and Extraction in Digital Video Frame Based on SVM and ICA

Video caption could be used to index video stream with high-level semantics since it implied lots of semantics inherently. The prior work of caption location and extraction considers how to define good caption features and neglects the self-generalization of classifier machine thereof. In order to overcome this limitation, an algorithm firstly localization and extraction video caption using support vector machine (SVM) and independent component analysis (ICA) is presented. In this algorithm, the raw video frame is segmented into N * N sub-blocks, and each block is identified either a caption block or a non-caption block; then mutually high-order independent ICA features are used to train a support vector machine classifier; finally the location and extraction of video caption can be finished automatically with pyramid model and de-noising techniques by each trained support vector machine classifier. Because support vector machine holds excellent generalization of classification with non-enough samples and independent component features are naturally high order independent each other, compared to other algorithms, the experiment data shows this method works well.