Fast Dictionary Matching for Content-based Image Retrieval

TL;DR

This paper introduces a fast method for content-based image retrieval that uses a descriptor dictionary and keypoint structure comparison to find similar image regions despite variations.

Contribution

The paper presents a novel approach combining descriptor dictionaries with structural analysis of interest points for improved image retrieval accuracy.

Findings

Achieved good performance in image retrieval tasks.

Effectively identified similar image regions with different scenes.

Utilized tolerance in descriptor values for matching.

Abstract

This paper describes a method for searching for common sets of descriptors between collections of images. The presented method operates on local interest keypoints, which are generated using the SURF algorithm. The use of a dictionary of descriptors allowed achieving good performance of the content-based image retrieval. The method can be used to initially determine a set of similar pairs of keypoints between images. For this purpose, we use a certain level of tolerance between values of descriptors, as values of feature descriptors are almost never equal but similar between different images. After that, the method compares the structure of rotation and location of interest points in one image with the point structure in other images. Thus, we were able to find similar areas in images and determine the level of similarity between them, even when images contain different scenes.