Feature-Based Adaptive Tolerance Tree (FATT): An Efficient Indexing Technique for Content-Based Image Retrieval Using Wavelet Transform

TL;DR

This paper proposes the Feature-Based Adaptive Tolerance Tree (FATT), an efficient indexing method using wavelet transform for content-based image retrieval, significantly improving retrieval performance on large image databases.

Contribution

The paper introduces FATT, a novel indexing technique utilizing wavelet features for efficient large-scale image retrieval, outperforming existing methods substantially.

Findings

FATT outperforms M-tree by up to 200%.

FATT outperforms Slim-tree by up to 120%.

FATT outperforms HCT by up to 89%.

Abstract

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.