A MICRO-GRAPH RETRIEVAL SYSTEM FOR CONIFEROUS WOODS USING MULTIPLE METHODS

Inspired by the successful application of deep convolutional neural network, a coniferous micro-graphs retrieval framework based on deep learning and image processing technology is proposed. The idea of the proposed framework is that the texture feature of representing three section surfaces can be learned and classified by a fully CNN, and the canals can be deep learned by an U-net CNN when the data labels are available. In addition, the image processing technologies are also proposed to identify whether the growth ring boundaries are distinct and whether there is a "window-like" cross-field pitting. Finally, a coniferous micro-graphs retrieval system is realized based the proposed methods. Experimental results demonstrate that this system outperforms in terms of recognition accuracy. In addition, the system can be further developed into more intelligent coniferous retrieval system that can automatically identify more coniferous microscopic features, so as to obtain more accurate retrieval results.