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Forest species recognition using macroscopic images

机译：使用宏观图像识别森林物种

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The recognition of forest species is a very challenging task that generally requires well-trained human specialists. However, few reach good accuracy in classification due to the time taken for their training; then they are not enough to meet the industry demands. Computer vision systems are a very interesting alternative for this case. The construction of a reliable classification system is not a trivial task, though. In the case of forest species, one must deal with the great intra-class variability and also the lack of a public available database for training and testing the classifiers. To cope with such a variability, in this work, we propose a two-level divide-and-conquer classification strategy where the image is first divided into several sub-images which are classified independently. In the lower level, all the decisions of the different classifiers, trained with different features, are combined through a fusion rule to generate a decision for the sub-image. The higher-level fusion combines all these partial decisions for the sub-images to produce a final decision. Besides the classification system we also extended our previous database, which now is composed of 41 species of Brazilian flora. It is available upon request for research purposes. A series of experiments show that the proposed strategy achieves compelling results. Compared to the best single classifier, which is a SVM trained with a texture-based feature set, the divide-and-conquer strategy improves the recognition rate in about 9 percentage points, while the mean improvement observed with SVMs trained on different descriptors was about 19 percentage points. The best recognition rate achieved in this work was 97.77 %.
机译：森林物种的识别是一项非常具有挑战性的任务，通常需要训练有素的人类专家。但是，由于培训时间长，很少有人能达到良好的分类精度；那么它们还不足以满足行业需求。对于这种情况，计算机视觉系统是非常有趣的替代方法。但是，构建可靠的分类系统并不是一件容易的事。就森林物种而言，必须处理巨大的类内变异性，而且还缺乏用于训练和测试分类器的公共可用数据库。为了应对这种可变性，在这项工作中，我们提出了一种两级分而治之的分类策略，即首先将图像分为几个独立分类的子图像。在较低级别，通过融合规则将经过不同特征训练的不同分类器的所有决策组合在一起，以生成子图像的决策。较高级别的融合将所有这些部分决定合并为子图像，以产生最终决定。除了分类系统，我们还扩展了以前的数据库，该数据库现在由41种巴西植物组成。可应要求提供用于研究目的。一系列实验表明，所提出的策略取得了令人信服的结果。与最佳单分类器（使用基于纹理的特征集训练的SVM）相比，分而治之策略将识别率提高了约9个百分点，而在不同描述符上训练的SVM所观察到的平均改善约为19个百分点。这项工作获得的最佳识别率是97.77％。

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