Color Image Retrieval Using Fuzzy Measure Hamming and S-Tree

TL;DR

This paper presents a novel image retrieval system based on fuzzy signatures derived from HSV color features, utilizing Fuzzy Hamming Distance and S-tree data structure to improve search efficiency and accuracy in large image databases.

Contribution

It introduces a new fuzzy signature representation for images, combined with Fuzzy Hamming Distance and S-tree, enhancing retrieval speed and reducing storage in content-based image retrieval systems.

Findings

Effective retrieval on a database of over 10,000 images

Reduced storage space and faster search times

High accuracy in image similarity assessment

Abstract

This chapter approaches the image retrieval system on the base of the colors of image. It creates fuzzy signature to describe the color of image on color space HSV and builds fuzzy Hamming distance (FHD) to evaluate the similarity between the images. In order to reduce the storage space and speed up the search of similar images, it aims to create S-tree to store fuzzy signature relies on FHD and builds image retrieval algorithm on S-tree. Then, it provides the content-based image retrieval (CBIR) and an image retrieval method on FHD and S-tree. Last but not least, based on this theory, it also presents an application and experimental assessment of the process of querying similar image on the database system over 10,000 images.