基于内容的图像检索界面设计

目录

摘要 (2)

Abstract (3)

第一章绪论 (4)

1.1 引言 (4)

1.2 CBIR的研究目的及意义 (5)

1.3 CBIR的研究现状 (6)

第二章 CBIR系统环境及框架 (8)

2.1 前言 (8)

2.2 CBIR的系统功能 (9)

2.3 CBIR的实例系统 (11)

第三章基于Oracle数据库的图像检索系统设计 (14)

3.1系统组织结构 (14)

3.2 数据库的设计及系统调用方法 (15)

3.3 系统交互界面的设计与实现 (18)

第四章结束语 (20)

4.1 工作总结 (20)

4.2进一步的工作 (20)

致谢.......................................................................................... 错误！未定义书签。参考文献. (21)

摘要

基于内容的图像检索是90年代以来逐渐兴起的一个新的研究方向。传统的图像检索方法是以数据库技术为基础、以大工作量的人工标注为代价的基于文本的检索。而我们要研究的基于内容的图像检索，就是要以计算机视觉技术为依托，根据图像的视觉特征(内容)，以模式匹配的方法进行以计算机为主导的图像检索。它大大减少了人工标注的沉重负担，提高了检索的速度和效率，为图像检索的应用提供了更广阔的前景。

首先是构建一个图像检索系统的最基本技术，包括图像的预处理，图像的颜色、纹理特征的提取及表示，视觉特征相似度量及两图像间的距离计算。

其次我们介绍了为提高系统效率而采用的性能优化方法，它可以从提高检索的准确度和速度两个角度考虑，分别对应于将人的因素引入检索过程的自相关反馈方法和事先对图像集进行聚类以减少检索的计算量的矢量量化方法。

总之，本文在重点分析我们系统的基础上，还对国内外其它的典型系统作了介绍；在介绍系统中用到的一些关键技术实现方法的同时，详细分析了其背景知识和理论依据；描述了系统的总体结构。

关键词：基于内容的图像检索, 颜色特征，纹理特征，数据库。

Abstract

Content Based Image Retrieval (CBIR) has been a very active research area since 1990's, with the thrust from two major research communities, Database Management and Computer Vision. The traditional approach of Image Retrieval is based on the technology of Database Management Systems (DBMS),with the cost of heavy burden of manual annotation. The proposed Content Based Image Retrieval, however, is a new approach based on Computer Vision, Pattern Recognition which perform the computer-centered image retrieval according to the content of images.

First, the basic techniques for establishing the pratical CBIR system, such as, image processing, the extracting of visual features for image and the computing of similarity between visual features of different images and furthermore the computing of distance between images.

Second, the advanced approach to improve system performance. They can be divided into two categories: one for improving the accuracy of retrieval and the other for improving the speed of retrieval, in our system, former method is relevance feedback and the latter is vector quantitation.

Later on , using semantic feature of images in CBIR system. The model for combining semantic feature and visual features of images into one system is proposed In conclusion, the purpose of this paper is trying to be a comprehensive article for CBIR system: Besides concentrate on our system, we introduced some other famous systems too. In addition to presented some important methods used in our system, we also get to the bottom of their theoretical origination and background knowledge. There are also some detailed analyses of typical algorithms and necessary experiment results in this paper.

Keywords: Content Based Image Retrieval, Color, Texture, Database