Multi-scale Forest Species Recognition Systems for Reduced Cost

TL;DR

This paper investigates methods to significantly reduce the computational cost of forest species recognition systems while maintaining or improving accuracy, through local and global cost reduction strategies evaluated on real datasets.

Contribution

It introduces combined local and global cost reduction techniques for forest species recognition, achieving over 95% cost savings with improved accuracy.

Findings

Cost reduced to less than 1/20 of original

Recognition accuracy improved over baseline

Effective cost reduction at both feature extraction and classification levels

Abstract

This work focuses on cost reduction methods for forest species recognition systems. Current state-of-the-art shows that the accuracy of these systems have increased considerably in the past years, but the cost in time to perform the recognition of input samples has also increased proportionally. For this reason, in this work we focus on investigating methods for cost reduction locally (at either feature extraction or classification level individually) and globally (at both levels combined), and evaluate two main aspects: 1) the impact in cost reduction, given the proposed measures for it; and 2) the impact in recognition accuracy. The experimental evaluation conducted on two forest species datasets demonstrated that, with global cost reduction, the cost of the system can be reduced to less than 1/20 and recognition rates that are better than those of the original system can be achieved.