Image Feature Information Extraction for Interest Point Detection: A Review

TL;DR

This paper provides a comprehensive review of image feature information extraction techniques for interest point detection, including taxonomy, evaluation, unresolved issues, and future directions in the field.

Contribution

It introduces a systematic taxonomy of IFI extraction methods, evaluates 18 state-of-the-art approaches, and discusses unresolved challenges and future research directions.

Findings

Evaluation of 18 state-of-the-art interest point detection methods

Identification of main unresolved issues in IFI extraction

Discussion of datasets and evaluation standards

Abstract

Interest point detection is one of the most fundamental and critical problems in computer vision and image processing. In this paper, we carry out a comprehensive review on image feature information (IFI) extraction techniques for interest point detection. To systematically introduce how the existing interest point detection methods extract IFI from an input image, we propose a taxonomy of the IFI extraction techniques for interest point detection. According to this taxonomy, we discuss different types of IFI extraction techniques for interest point detection. Furthermore, we identify the main unresolved issues related to the existing IFI extraction techniques for interest point detection and any interest point detection methods that have not been discussed before. The existing popular datasets and evaluation standards are provided and the performances for eighteen state-of-the-art approaches are evaluated and discussed. Moreover, future research directions on IFI extraction techniques for interest point detection are elaborated.