A multiple feature vector framework for forest species recognition
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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In this work we address the problem of forest species recognition which is a very challenging task and has several potential applications in the wood industry. The first contribution of this work is a database composed of 22 different species of the Brazilian flora that has been carefully labeled by expert in wood anatomy. In addition, in this work we demonstrate through a series of comprehensive experiments that color-based features are quite useful to increase the discrimination power for this kind of application. Last but not least, we propose a segmentation approach so that a wood can be locally processed to mitigate the intra-class variability featured in some classes. Such an approach also brings important contribution to improve the final performance in terms of classification.