基于跨域深度学习的复杂视频场景分类方法研究

项目名称： 基于跨域深度学习的复杂视频场景分类方法研究

项目编号： No.61305048

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 李和平

作者单位： 中国科学院自动化研究所

项目金额： 26万元

中文摘要： 深度学习能够通过组合底层特征形成更加抽象的上层特征，是一种极具前途的多层网络学习算法。当应用于复杂视频场景分类建模时，由于场景的多语义性和拍摄条件的多变性，它需要大量样本以学习深度模型的参数，但是,在复杂视频应用领域往往只能有效获取少量标记样本，需要借助网络辅助资源，此时，深度学习又面临跨域特征提取和跨域建模的问题。因此，本项目拟开展基于跨域深度学习的复杂视频场景分类方法研究,重点研究四个方面的内容：（1）大规模网络辅助资源样本集的自动构造方法；（2）基于跨域资源深度学习的场景高层语义特征提取与表示理论；（3）基于跨域样本集的深度模型参数有效学习策略和快速计算方法；（4）基于上层分类信息融合的复杂视频场景分类器跨域建模方法。通过本项目的实施，将在理论上形成系统的基于跨域资源深度学习的复杂视频场景分类理论和方法，同时形成一个原型系统，在视频监控和数字电视视频场景分析领域得到初步应用。

中文关键词： 计算机视觉；场景识别；深度神经网络；迁移学习；样本集构造

英文摘要： Deep learning, which can compose low-level features to form higher-level features, is a promising learning method about multi-layer network. When deep learning model is applied for classifying complex video scene, a large number of video scene samples are indispensable for training its parameters because there are a lot of semantic objects and tremendous variations from camera motion and background in the videos. Since manual labeling of video scenes is laborious and time-consuming, we can only get a small number of complex video samples which are not enough for us to train an effective video scene classification model with good classification performance and generalization capability.So we need a great number of auxiliary samples. However, after we get these samples from Internet, deep learning model will face other problems: how to effectively extract high-level semantic features and how to build video scene classification model by using the cross-domain samples. To solve these problems, we will study Complex Video Scene Classification Method Based on Deep Learning with Cross-Domain Sources in this project. And we will focus on exploring the following four key technologies:(1)the method for automatically constructing auxiliary scene dataset from Internet sources with many irrelevant samples,(2) the high-level

英文关键词： compute vision；scene recognition；deep neural network；transfer learning；sample dataset construction