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Rotation, shift and scale invariant wavelet features for content-based image retrieval and classification.

机译：旋转，平移和缩放不变小波特征，用于基于内容的图像检索和分类。

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摘要

Rapid continual advances in computer and network technologies coupled with the availability of relatively cheap high-volume data storage devices have effected the production of thousands of digital images or photos everyday, e.g. in World Wide Web. Many content based image retrieval (CBIR) systems have been proposed to cope with such huge image archives. However, most of the current content based image features for CBIR do not address the issues of orientation, position or scale variations of images. In practice, the presence of orientation, position or scale variations of images in digital image repositories could introduce problems in image retrieval. The neglect of such problems could result in a relatively poor performance of the current CBIR systems.; Discrete wavelet transforms have been shown to be effective for image analysis. But it is well known that one major drawback of discrete wavelet transforms is their lack of rotation, shift and scale invariance, and joint invariance. The work presented in this thesis focus on the extraction of an effective and robust rotation, shift, scale and joint invariant wavelet features for content-based image retrieval and classification. An adaptive rotation invariant wavelet packet transform and seven schemes for extracting the invariant wavelet features have been proposed. The invariant wavelet features are represented by the dominant energy signatures computed by the respective invariant wavelet coefficients generated. The feature extraction process is quite efficient, with an overall computational complexity of O(n · log n), where n is the number of pixels in the image. Experimental results for CBIR show that the proposed invariant wavelet feature can achieve a high retrieval precision and recall for general performance testing and can achieve perfect retrieval precision for invariance performance testing. Moreover, experimental results for content based image classification (CBIC) show that the proposed invariant wavelet features can achieve very high classification accuracy for both general performance testing and invariance performance testing. The results also show that the proposed invariant wavelet features are quite robust to noise and outperform other image classification methods significantly.
机译：计算机和网络技术的快速持续发展，加上相对便宜的大容量数据存储设备的可用性，已经每天产生成千上万个数字图像或照片，例如在万维网上。已经提出了许多基于内容的图像检索（CBIR）系统来应对如此庞大的图像档案。但是，当前用于CBIR的大多数基于内容的图像功能都无法解决图像的方向，位置或比例变化的问题。实际上，数字图像存储库中图像的方向，位置或比例变化的存在可能会在图像检索中引入问题。忽略这些问题可能导致当前CBIR系统的性能相对较差。离散小波变换已被证明对图像分析有效。但是众所周知，离散小波变换的一个主要缺点是它们缺乏旋转，位移和比例不变性以及联合不变性。本文提出的工作集中在为基于内容的图像检索和分类中提取有效且鲁棒的旋转，移位，缩放和联合不变小波特征。提出了一种自适应旋转不变小波包变换和七种提取不变小波特征的方案。不变小波特征由主导能量特征表示，该主导能量特征由产生的各个不变小波系数计算。特征提取过程非常高效，总体计算复杂度为O（ n ·log n ），其中 n 是像素数在图像中。 CBIR的实验结果表明，所提出的不变小波特征可以实现较高的检索精度和召回率，适用于一般性能测试，并且可以实现完美的检索精度以进行不变性能测试。此外，基于内容的图像分类（CBIC）的实验结果表明，对于一般性能测试和不变性能测试，所提出的不变小波特征均可以实现非常高的分类精度。结果还表明，所提出的不变小波特征对噪声非常鲁棒，并且明显优于其他图像分类方法。

著录项

  • 作者

    Pun, Chi Man.;

  • 作者单位

    Chinese University of Hong Kong (People's Republic of China).;

  • 授予单位 Chinese University of Hong Kong (People's Republic of China).;
  • 学科 Computer Science.; Engineering Electronics and Electrical.; Mathematics.
  • 学位 Ph.D.
  • 年度 2002
  • 页码 127 p.
  • 总页数 127
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自动化技术、计算机技术;无线电电子学、电信技术;数学;
  • 关键词

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