Multi-scale Forest Species Recognition Systems for Reduced Cost
This addresses cost efficiency for forest species recognition applications, but it is incremental as it builds on existing methods.
This work tackled the problem of high computational cost in forest species recognition systems by investigating local and global cost reduction methods, achieving a cost reduction to less than 1/20 while improving recognition rates compared to the original system.
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.