Accelerated Sub-Image Search For Variable-Size Patches Identification Based On Virtual Time Series Transformation And Segmentation

TL;DR

This paper introduces a fast, neural network-free method for sub-image search that transforms images into time series and segments them, significantly reducing search times while maintaining visual accuracy.

Contribution

It presents a novel acceleration technique for sub-image search using virtual time series transformation and segmentation, applicable to variable-size patches.

Findings

Achieves up to 100x speedup over exhaustive search

Maintains comparable visual quality in results

Applicable to both fixed-size and variable-size sub-image identification

Abstract

This paper addresses two tasks: (i) fixed-size objects such as hay bales are to be identified in an aerial image for a given reference image of the object, and (ii) variable-size patches such as areas on fields requiring spot spraying or other handling are to be identified in an image for a given small-scale reference image. Both tasks are related. The second differs in that identified sub-images similar to the reference image are further clustered before patches contours are determined by solving a traveling salesman problem. Both tasks are complex in that the exact number of similar sub-images is not known a priori. The main discussion of this paper is presentation of an acceleration mechanism for sub-image search that is based on a transformation of an image to multivariate time series along the RGB-channels and subsequent segmentation to reduce the 2D search space in the image. Two variations of the acceleration mechanism are compared to exhaustive search on diverse synthetic and real-world images. Quantitatively, proposed method results in solve time reductions of up to 2 orders of magnitude, while qualitatively delivering comparative visual results. Proposed method is neural network-free and does not use any image pre-processing.