基于成分分解与相似性分析的图像异常检测研究

项目名称： 基于成分分解与相似性分析的图像异常检测研究

项目编号： No.61472220

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 计算机科学学科

项目作者： 郭强

作者单位： 山东财经大学

项目金额： 84万元

中文摘要： 异常区域的自动检测是图像内容理解与分析的关键，在公共安全、工业生产以及医疗诊断等领域具有广泛的应用需求和很好的产业前景。快速准确的异常检测是图像处理和计算机视觉等领域没有解决好的热点和难点问题。本项目以图像数据可靠性的提升和冗余信息的有效利用为切入点，探索图像成分不同特性表示的理论，研究基于这些特性的图像成分差异化异常检测理论和方法。研究主要围绕图像成分分解模型、成分异常检测、异常区域特征提取以及模型与方法的快速处理等关键问题开展，四个研究内容构成一个有机整体，形成异常检测的整体解决方案。本项目的研究将为快速准确的图像异常检测提供新的理论和方法，以解决异常检测所面临的准确性低、实时性差等问题，并为相关应用领域提供稳定有效的新技术。

中文关键词： 异常检测；图像分解；自相似性；特征提取；图像表示

英文摘要： Automatic abnormal region detection is one of the key techniques in image content understanding and analysis, which has a wide range of requirements and a well prospect of the industry in various applications such as public security, industrial production and medical diagnosis. Fast and accurate anomaly detection is a hot and difficult problem that is not well solved in image processing and computer vision fields. This project uses the reliability enhancement of image data and the effective utilization of redundant information as the breakthrough points, explores the theoretical representations of different characteristics of image components, and studies the theory and method based on these mathematical representations for anomaly detection. Therefore, this study mainly focuses on the theory for image component decomposition, anomaly detection methods for different components, feature extraction of abnormal regions, and fast implementations of relative algorithms. By fusing these four key issues, a total solution for anomaly detection is expected to form. The problems faced by anomaly detection, such as low accuracy and poor real-time, are expected to be solved by this total solution. The research achievements of this project will provide a novel theory and method for the real-time anomaly detection with high accuracy, and also provide an efficient technique for relative applications.

英文关键词： Anomaly detection;Image decomposition;Self-similarity;Feature extraction;Image representation