This work focuses on cost reduction methods, applied on forest species recognition systems as a case-study. Current state-of-the-art shows that the accuracy of these systems, generally employing texture recognition approaches, have increased considerably in the past years. However, the cost in time to perform the recognition of input samples has also increased proportionally. By taking into account previous research that demonstrated that cost reduction at classification level can provide much faster systems, in this work we focus on proposing metrics to measure the impact of cost reduction at another important module of image recognition system, i.e the feature extraction stage, and on how to measure cost reduction at global level, i.e. combining cost reduction at both feature extraction and classification. The evaluation of the proposed metrics on a forest species dataset demonstrated that, with global cost reduction, not only the cost of the system can be reduced to less than 1/20, but also the recognition rates can be improved.