Especially
in recent years, deep learning has become a very effective tool for object
identification. However, in general, the automatic object identification tends
not to work well on ambiguous, amorphous objects such as vegetation. In this
study, we developed a simple but effective approach to identify ambiguous
objects and applied the method to several moss species. The technique called
chopped picture method, where teacher images are systematically dissected into
numerous small squares. As a result, the model correctly classified 3 moss
species and “non-moss” objects in test images with accuracy more than 90%.
Using this approach will help progress in computer vision studies for various
ambiguous objects.
References
[1]
Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2012) ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM, 60, 84-90. https://doi.org/10.1145/3065386
[2]
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V. and Rabinovich, A. (2015) Going Deeper with Convolutions. https://arxiv.org/pdf/1409.4842v1.pdf
[3]
Wang, D., Khosla, A., Gargeya, R., Irshad, H. and Beck, A. (2016) Deep Learning for Identifying Metastatic Breast Cancer. https://arxiv.org/pdf/1606.05718v1.pdf
[4]
Ueya Kato Landscape Co., Ltd. (2007) Annual Fostering Techniques of Existing Plants of Murin-An Garden as a National Place Scenic Beauty: For Preserving the Spatial Characteristics of Original Sensitivity of Aritomo Yamagata. Kyoto Municipal Government, Kyoto.
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R Core Team (2016) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R-project.org/
[6]
Barker, J. and Prasanna, S. (2016) Deep Learning for Object Detection with DIGITS. https://devblogs.nvidia.com/parallelforall/deep-learning-object-detection-digits/
