1 research outputs found
Multi-scale Forest Species Recognition Systems for Reduced Cost
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