数据驱动的层次场景序列识别模型研究

针对层次场景图像序列，本文提出了一种数据驱动的基于快速序列视觉表述任务（rapid serial visual presentation task，RSVP）的场景识别模型. 首先基于金字塔模型提取三层尺度图像块，然后构建包括全局和局部特征的词汇字典，接着分别利用生成模型和判决模型训练视觉词汇，最后通过神经网络从图像块标记中获得场景类别. 实验表明算法能够获得更为精确的分类结果.

Data Driven Hierarchical Serial Scene Classification Framework

Scene classification is a complicated task, because it includes much content and it is difficult to capture its distribution. A novel hierarchical serial scene classification framework is presented in this paper. At first, we use hierarchical feature to present both the global scene and local patches containing specific objects. Hierarchy is presented by space pyramid match, and our own codebook is built by two different types of words. Secondly, we train the visual words by generative and discriminative methods respectively based on space pyramid match, which could obtain the local patch labels efficiently. Then, we use a neural network to simulate the human decision process, which leads to the final scene category from local labels. Experiments show that the hierarchical serial scene image representation and classification model obtains superior results with respect to accuracy.