Study on the adaptive high-dimensional information retrieval based on learning and its applications

本课题研究了基于内容的图像分析方法，通过实例学习概率生成模型。目前，网络上存在着各种各样的媒体信息，如图像、电影等，要有效地获取这些媒体信息，就需要基于内容的信息检索方法。本文实现了基于内容的图像分类、分割、检索和图像标注。为此，我们利用概率主题模型。在模型中引入潜在主题变量来表示图像的内容。在概率主题模型中，我们主要使用混合模型，pLSA（概率潜在语义分析），LDA（潜在Ditichlet分配）的图像分析。对于从例子中学习模型参数，我们使用EM算法（对于混合模型和pLSA）和变分方法（对于IDA）。基于主题模型，我们提出了图像分类的基础上的估计的条件概率p（第一类图像），自底向上的图像分割的文档图像，基于内容的图像检索和图像标注的码头词的基础上。在图像标注和基于查询词的图像检索中，需要建立图像和关键词的概率模型。我们提出了几个模型的任务。我们使用图像数据库，包括Caltech101数据库和MIT LabelMe数据库，通过实验来检验所提出的方法的有效性。

In this research project, we investigate the content- based image analysis via learning probabilistic generative model from examples Nowadays, there exists various kinds of media information, such images, movies, on the network 'lb efficiently access to such media information, the method of content-based information retrieval is required. 'lb realize the method, in this research, we deal with the content-based image categorization, segmentation, retrieval and image annotation. For that purpose we exploit the probabilistic topic models. In the models latent topic variables am introduced to represent the content of the image. Among the probabilistic topic models, we mainly use mixture model, pLSA (probabilistic latent semantic analysis), LDA (latent Ditichlet allocation) for image analysis. For learning model parameters from examples, we use EM algorithm (for mixture model and pLSA) and variational methods (for IDA). Based on the topic models, we propose methods for image categorization based on the estimation of the conditional probability p(category I image), bottom-up image segmentation for document images, and content-based image retrieval and image annotation based on the quay words. For image annotation and image retrieval by query word, probabilistic model of image and keywords is required. We propose several models for the task. We examine the effectiveness of the proposed methods by experiments using image database, including Caltech101 database and MIT LabelMe database.