Fractal triangular search: a metaheuristic for image content search

TL;DR

This paper introduces a fractal-based variable neighbourhood search algorithm for image content retrieval, demonstrating superior speed and accuracy over existing metaheuristics, especially on larger images.

Contribution

The authors develop a novel fractal triangular search method that enhances image content search efficiency compared to prior metaheuristics.

Findings

FTS outperforms state-of-the-art metaheuristics in speed and accuracy.

FTS is >8% faster in most cases and >22% faster on average than second-best methods.

Performance advantage increases with image size.

Abstract

This work proposes a variable neighbourhood search (FTS) that uses a fractal-based local search primarily designed for images. Searching for specific content in images is posed as an optimisation problem, where evidence elements are expected to be present. Evidence elements improve the odds of finding the desired content and are closely associated to it in terms of spatial location. The proposed local search algorithm follows the fashion of a chain of triangles that engulf each other and grow indefinitely in a fractal fashion, while their orientation varies in each iteration. The authors carried out an extensive set of experiments, which confirmed that FTS outperforms state-of-the-art metaheuristics. On average, FTS was able to locate content faster, visiting less incorrect image locations. In the first group of experiments, FTS was faster in seven out of nine cases, being >8% faster on average, when compared to the second best search method. In the second group, FTS was faster in six out of seven cases, and it was >22% faster on average when compared to the approach ranked second best. FTS tends to outperform other metaheuristics substantially as the size of the image increases.