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

arXiv: https://arxiv.org/abs/2410.15425 · HTML: https://mlsanity.com/paper/2410.15425/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 20 Oct 2024.

## Builds on (most cited first, 6 of 6)

- [Vision Transformers For Weeds and Crops Classification Of High Resolution UAV Images](https://mlsanity.com/paper/2109.02716/) (2021)
- [Integrated Time Series Summarization and Prediction Algorithm and its Application to COVID-19 Data Mining](https://mlsanity.com/paper/2005.00592/) (2020)
- [Path Planning for Spot Spraying with UAVs Combining TSP and Area Coverages](https://mlsanity.com/paper/2408.08001/) (2024)
- [Coupling of Crop Assignment and Vehicle Routing for Harvest Planning in Agriculture](https://mlsanity.com/paper/1703.08999/) (2017)
- [GPU-accelerated Logistics Optimisation for Biomass Production with Multiple Simultaneous Harvesters Tours, Fields and Plants](https://mlsanity.com/paper/1909.10286/) (2019)
- [A posteriori Trading-inspired Model-free Time Series Segmentation](https://mlsanity.com/paper/1912.06708/) (2019)

## Abstract (arXiv)

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.