Optimizing Auto-correlation for Fast Target Search in Large Search Space

TL;DR

This paper introduces OptA, an algorithm that non-uniformly blurs images to accelerate target search in large datasets without losing accuracy, by optimizing auto-correlation computations.

Contribution

It proposes a novel non-uniform blurring technique combined with an Efficient Group Size algorithm to enhance template matching speed while maintaining accuracy.

Findings

Significant speedup in target search demonstrated on satellite and aerial images.

OptA outperforms six state-of-the-art methods in computational efficiency.

No accuracy loss observed despite increased processing speed.

Abstract

In remote sensing image-blurring is induced by many sources such as atmospheric scatter, optical aberration, spatial and temporal sensor integration. The natural blurring can be exploited to speed up target search by fast template matching. In this paper, we synthetically induce additional non-uniform blurring to further increase the speed of the matching process. To avoid loss of accuracy, the amount of synthetic blurring is varied spatially over the image according to the underlying content. We extend transitive algorithm for fast template matching by incorporating controlled image blur. To this end we propose an Efficient Group Size (EGS) algorithm which minimizes the number of similarity computations for a particular search image. A larger efficient group size guarantees less computations and more speedup. EGS algorithm is used as a component in our proposed Optimizing auto-correlation (OptA) algorithm. In OptA a search image is iteratively non-uniformly blurred while ensuring no accuracy degradation at any image location. In each iteration efficient group size and overall computations are estimated by using the proposed EGS algorithm. The OptA algorithm stops when the number of computations cannot be further decreased without accuracy degradation. The proposed algorithm is compared with six existing state of the art exhaustive accuracy techniques using correlation coefficient as the similarity measure. Experiments on satellite and aerial image datasets demonstrate the effectiveness of the proposed algorithm.