# PARSEG: a computationally efficient approach for statistical validation of botanical seeds’ images

## TL;DR

PARSEG is a method that reduces the computational cost of validating binary images by using a small sample of pixels without losing accuracy.

## Contribution

PARSEG introduces a novel statistical validation approach for binary images using minimal pixel sampling.

## Key findings

- PARSEG reduces computing time by about 90% while maintaining validation precision.
- The method uses only 4% of the original pixels for validation in seed recognition images.
- PARSEG achieves similar precision to full-image validation in images with 13 million pixels.

## Abstract

Human recognition and automated image validation are the most widely used approaches to validate the output of binary segmentation methods but, as the number of pixels in an image easily exceeds several million, they become highly demanding from both practical and computational standpoint. We propose a method, called PARSEG, which stands for PArtitioning, Random Selection, Estimation, and Generalization; being the basic steps within this procedure. Suggested method enables us to perform statistical validation of binary images by selecting the minimum number of pixels from the original image to be used for validation without deteriorating the effectiveness of the validation procedure. It utilizes binary classifiers to accomplish image validation and selects the optimal sample of pixels according to a specific objective function. As a result, the computational complexity of the validation experiment is substantially reduced. The procedure’s effectiveness is illustrated by considering images composed of approximately 13 million pixels from the field of seed recognition. PARSEG provides roughly the same precision of the validation process when extended to the entire image, but it utilizes only about 4% of the original number of pixels, thus reducing, by about 90%, the computing time required to validate a binary segmented image.