基于小波变换的自适应容差树（FATT）：一种有效的基于内容的图像检索索引技术
Feature-Based Adaptive Tolerance Tree (FATT): An Efficient Indexing Technique for Content-Based Image Retrieval Using Wavelet Transform
摘要：
本文介绍了一种新的索引和访问方法，称为基于特征的自适应容差树（FATT），提出了使用小波变换来有效地组织大型图像数据集，并支持流行的图像访问机制，如基于内容的图像检索（CBIR）。然而，这种方法不再适用于图像，因为图像的数字表示并不传达图像的真实性。当数据库非常大时，图像检索变得困难。本文针对这些问题，并提出了一种新的索引技术，基于特征的自适应容差树（FATT），这是旨在带来一个有效的解决方案，特别是对大型数据库的索引。建议的索引方案，然后使用与查询的图像内容，以实现最终目标，从用户的角度来看，是检索所有相关的图像。FATT索引技术，利用二维离散小波变换（2DDWT）提取图像的特征，并由特征的行列式值生成索引码。基于2D-DWT的多分辨率分析技术可以将图像分解为不同尺度的分量，使得最粗尺度分量包含全局近似信息，而更细尺度分量包含细节信息。实验结果表明，FATT比M-tree的性能提高了200%，比Slim-tree的性能提高了120%，比HCT的性能提高了89%。采用FATT索引技术提高了数据存储和检索的效率。
英文摘要：
This paper introduces a novel indexing and access method, called Feature- Based Adaptive Tolerance Tree (FATT), using wavelet transform is proposed to organize large image data sets efficiently and to support popular image access mechanisms like Content Based Image Retrieval (CBIR).Conventional database systems are designed for managing textual and numerical data and retrieving such data is often based on simple comparisons of text or numerical values. However, this method is no longer adequate for images, since the digital presentation of images does not convey the reality of images. Retrieval of images become difficult when the database is very large. This paper addresses such problems and presents a novel indexing technique, Feature Based Adaptive Tolerance Tree (FATT), which is designed to bring an effective solution especially for indexing large databases. The proposed indexing scheme is then used along with a query by image content, in order to achieve the ultimate goal from the user point of view that is retrieval of all relevant images. FATT indexing technique, features of the image is extracted using 2-dimensional discrete wavelet transform (2DDWT) and index code is generated from the determinant value of the features. Multiresolution analysis technique using 2D-DWT can decompose the image into components at different scales, so that the coarest scale components carry the global approximation information while the finer scale components contain the detailed information. Experimental results show that the FATT outperforms M-tree upto 200%, Slim-tree up to 120% and HCT upto 89%. FATT indexing technique is adopted to increase the efficiently of data storage and retrieval.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
