基于内容的图像检索与过滤关键技术研究
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摘要
随着互联网上图像资源的日益丰富，如何建立基于内容的图像描述、检索、过滤机制已经成为目前互联网有效应用需要迫切解决的问题之一。本文利用图像处理、模式识别、计算机视觉与数据库等技术，针对基于内容图像检索与过滤中的关键问题展开研究。在图像检索方面，本文的研究目标是：提高基于内容图像检索系统的精度，以满足Web环境下不断增长的基于内容图像检索的需求，研究重点是图像检索中的相关反馈技术和面向相关反馈的图像语义模型。在图像过滤方面，本文探讨了基于内容图像过滤的概念、特点和过滤模型，针对互联网上传播色情图像与录像比较严重的现象，研究了基于内容的敏感图像过滤问题。取得的主要研究成果包括：
(1)提出了一种自适应的相关反馈方法——Rich Get Richer(简称RGR)，它是贝叶斯理论与相关反馈策略相结合的图像检索方法。该方法结合了相关反馈图像检索系统的时序特性，动态地修正交互信息给系统带来的影响，在某种程度上使图像检索结果与人的主观感知更加接近，因此具有自适应性。该方法具有计算简单、准确率高、响应快等优点。
(2)针对相关反馈图像检索系统，提出了一种新的图像语义模型——图像间语义模型。试图在不进行图像分割和关键词标注的情况下，通过分析图像检索过程而达到获取图像语义信息的目的。提出了基于互信息的图像间语义关系学习方法和基于图像关联因子的图像间语义关系学习方法。
(3)对图像语义聚类问题进行了初步探索，提出了两种利用图像间语义关系进行语义聚类的方法，基于互信息的语义聚类方法和基于关联规则超图分割的语义聚类方法。上述方法在一定程度上突破了传统的基于视觉特征图像聚类，通过对用户的访问历史信息进行分析，建立语义相关图像事务数据库，实现了图像数据库的语义聚类，为图像数据库的管理提供了新的思路。
(4)在图像过滤方面，提出了一个多层次的特定类型图像过滤方法。该方法根据敏感图像的显著特征，通过建立有效描述被过滤图像特征的肤色模型，结合支持向量机和最近邻算法实现了对敏感图像的有效过滤。在此基础上，进一步针对多层次特定类型图像过滤方法误检率高的问题，提出了基于多特征特定类型图像过滤方法。实验结果表明我们建立的模型是有效的。
(5)实现了图像检索与过滤原型系统。
Building up content-based image descriptions, retrieval and filtering on the Internet is a crucial issue because of the recent explosion in the amount of online images. By using the technologies of image processing, pattern recognition, computer vision and database, this dissertation studies some key problems in the field of image retrieval and image filtering. In order to improve the performance of CBIR system, the research work concerning the image retrieval field of this dissertation focuses on relevance feedback method and semantic model oriented to image retrieval system with relevance feedback. In the following part of the dissertation, the concept, architecture and model of content-based image filtering has been discussed, and adult image filtering methods have been studied in order to reject offensive embedded images on web pages. The contributions of the dissertation are as follows:
(1) This dissertation presents a relevance feedback method for image retrieval —Rich Get Richer (RGR), which is based on Bayesian inference. It takes the time sequence characteristic of relevance feedback into account, and modifies the effect of relevance feedback to image retrieval system. So, this system has the ability to be adaptive, which makes the retrieval result consistent with the user's subjectivity. The experimental results have shown that the proposed approach can capture the user's information need more precisely and quickly.
(2) An image internal semantic model for the relevance feedback image retrieval system is proposed. It extracts the semantic information by analyzing relevance feedback image retrieval results without troubling image annotation and difficult image segmentation (Image segmentation is an open problem). In order to get the semantic correlation of images, two learning methods based-on mutual information and image association factor, are proposed.
(3) This dissertation presents two semantic clustering methods. One is based-on mutual information learning method, and the other is based-on association rule hypergraph partitioning algorithm. Different from the clustering method based-on visual feature, it analyzes the historic information of user retrievals and creates a semantic related image translation database. This provides a new way for image database management.
(4) For content-based image filtering, the dissertation presents a multi-layer filtering method, which creates an efficient color skin detection model according to the remarkable characteristic of adult images, and also uses support vector machine and K-nearest neighbor methods to reject offensive images. Experiments have shown that the method is efficient for adult image detection, but many normal images are also recognized as adult image. Therefore, the multi-feature filtering method is designed to resolve the problem. Experiments have shown that the multi-feature filtering method is very effective.
(1)提出了一种自适应的相关反馈方法——Rich Get Richer(简称RGR)，它是贝叶斯理论与相关反馈策略相结合的图像检索方法。该方法结合了相关反馈图像检索系统的时序特性，动态地修正交互信息给系统带来的影响，在某种程度上使图像检索结果与人的主观感知更加接近，因此具有自适应性。该方法具有计算简单、准确率高、响应快等优点。
(2)针对相关反馈图像检索系统，提出了一种新的图像语义模型——图像间语义模型。试图在不进行图像分割和关键词标注的情况下，通过分析图像检索过程而达到获取图像语义信息的目的。提出了基于互信息的图像间语义关系学习方法和基于图像关联因子的图像间语义关系学习方法。
(3)对图像语义聚类问题进行了初步探索，提出了两种利用图像间语义关系进行语义聚类的方法，基于互信息的语义聚类方法和基于关联规则超图分割的语义聚类方法。上述方法在一定程度上突破了传统的基于视觉特征图像聚类，通过对用户的访问历史信息进行分析，建立语义相关图像事务数据库，实现了图像数据库的语义聚类，为图像数据库的管理提供了新的思路。
(4)在图像过滤方面，提出了一个多层次的特定类型图像过滤方法。该方法根据敏感图像的显著特征，通过建立有效描述被过滤图像特征的肤色模型，结合支持向量机和最近邻算法实现了对敏感图像的有效过滤。在此基础上，进一步针对多层次特定类型图像过滤方法误检率高的问题，提出了基于多特征特定类型图像过滤方法。实验结果表明我们建立的模型是有效的。
(5)实现了图像检索与过滤原型系统。
Building up content-based image descriptions, retrieval and filtering on the Internet is a crucial issue because of the recent explosion in the amount of online images. By using the technologies of image processing, pattern recognition, computer vision and database, this dissertation studies some key problems in the field of image retrieval and image filtering. In order to improve the performance of CBIR system, the research work concerning the image retrieval field of this dissertation focuses on relevance feedback method and semantic model oriented to image retrieval system with relevance feedback. In the following part of the dissertation, the concept, architecture and model of content-based image filtering has been discussed, and adult image filtering methods have been studied in order to reject offensive embedded images on web pages. The contributions of the dissertation are as follows:
(1) This dissertation presents a relevance feedback method for image retrieval —Rich Get Richer (RGR), which is based on Bayesian inference. It takes the time sequence characteristic of relevance feedback into account, and modifies the effect of relevance feedback to image retrieval system. So, this system has the ability to be adaptive, which makes the retrieval result consistent with the user's subjectivity. The experimental results have shown that the proposed approach can capture the user's information need more precisely and quickly.
(2) An image internal semantic model for the relevance feedback image retrieval system is proposed. It extracts the semantic information by analyzing relevance feedback image retrieval results without troubling image annotation and difficult image segmentation (Image segmentation is an open problem). In order to get the semantic correlation of images, two learning methods based-on mutual information and image association factor, are proposed.
(3) This dissertation presents two semantic clustering methods. One is based-on mutual information learning method, and the other is based-on association rule hypergraph partitioning algorithm. Different from the clustering method based-on visual feature, it analyzes the historic information of user retrievals and creates a semantic related image translation database. This provides a new way for image database management.
(4) For content-based image filtering, the dissertation presents a multi-layer filtering method, which creates an efficient color skin detection model according to the remarkable characteristic of adult images, and also uses support vector machine and K-nearest neighbor methods to reject offensive images. Experiments have shown that the method is efficient for adult image detection, but many normal images are also recognized as adult image. Therefore, the multi-feature filtering method is designed to resolve the problem. Experiments have shown that the multi-feature filtering method is very effective.
引文
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