【作者】 张彤；

【导师】 陆哲明；

【作者基本信息】 哈尔滨工业大学 ， 仪器科学与技术， 2007， 硕士

【摘要】 图像的自动标注技术和基于语义的图像检索技术在多媒体信息检索领域有着重要的地位。图像标注满足了用户在海量图像数据库中进行关键词检索的需要。传统的基于文本的图像检索系统需要耗费大量人力进行手工标注,因此,研究人员提出了基于内容的图像检索技术,该技术直接从图像内容中提取颜色、纹理和形状等底层特征进行检索。但是,随着研究的深入,基于内容的图像检索遇到了一个严重的问题,即图像底层特征和语义概念之间存在着“语义鸿沟”。为了解决“语义鸿沟”这一难题,本文提出了基于矢量量化和潜在狄利克雷相结合的模型标注方法。首先,介绍了图像自动标注和检索的研究背景和意义,总结了当前各标注系统的构成及其优缺点。近年来,潜在狄利克雷分布(LDA)模型在文本信息检索领域得到了广泛的研究和应用,许多学者已经证明LDA模型对于离散数据的处理和降维有着显著的作用。不难看出,图像信息和文本信息有着惊人的相似性:1、数据的海量性和高维性,2、同一类图像中包含的相近的目标,而同一类文档含有大量相近的单词。本文详细论述了LDA模型的数学原理,提出了该模型在图像数据处理和图像语义检索中的应用方法。为了描述图像中的有意义区域,需要对图像的目标进行分割运算。本文的在回顾经典图像分割算法的计算速度和分割效果之后,选择了分水岭算法对图像进行分割,并针对该算法存在的过分割问题,提出了一种改进方法。接着,对分割后的图像区域提取了颜色、纹理和形状共18维特征,利用矢量量化技术将这些特征聚类压缩。聚类后的区域特征对应生成“码书”中的一个码字,压缩后得到的“码书”可以看成是图像库的一个语义词典。最后,本文使用MATLAB程序实现了上述方法,并对400幅Corel图像库进行了仿真实验。实验结果表明,本算法可以完成图像的自动标注和关键词检索。更多还原

【Abstract】 Automatic image annotation and semantic based image retrieval is essentially important for multimedia information retrieval. Image annotations allow users to access a large image database with textual queries. Traditional keyword-based image retrieval systems cost a large amount of human labors to annotate images; thereby the content-based image retrieval is brought forward, which tries to retrieve images directly and automatically based on their visual contents such as color, texture, and shape. However, content-based image retrieval faces a vital problem, namely“semantic gap”that exists between low level features and semantic concept.To solve this“semantic gap”problem, an automatic annotation method based on both vector quantization and Latent Dirichlet Allocation (LDA) has been presented in this thesis. First, the background and significance of automatic image annotation and semantic based retrieval are introduced. The structure of annotation models and their merits and defects are reviewed. In recent years, LDA model has been widely studied in the field of text based information retrieval. It has been approved by many researchers that LDA has a notable effect on discrete data processing and demission reduction. Obviously, there are amazing similarities between text data and image information: 1 both of their data are very huge and have high dimensions; 2 similar objects can be easily found in the same image collection and the synonyms always exist in one document corpus. In this thesis, mathematical principle of LDA model is elaborated, and the application method is presented for image data processing and semantic based image retrieval.In order to describe the meaningful region of images, the segmentation process is necessary. After comparison of the computation speed and segmentation results by different traditional image segmentation algorithm, the watershed algorithm is chosen. Since it has a problem of over-segmentation, a modified algorithm is proposed. Then, 18 features including color, texture, and shape are extracted from the regions after segmentation, which are clustered and condensed using vector quantization. Region features after clustering corresponds to“code words”in the“code book”. The condensed“code book”can be regarded as a semantic lexicon in image database.Finally, the method is realized on the platform of MATLAB and applied to the Corel database of 400 images. The results show that the method can complete image automatic annotation and keyword based retrieval.更多还原