Name
Abstract | ||
|---|---|---|
Forest species recognition has been traditionally addressed as a texture classification problem, and explored using standard texture methods such as Local Binary Patterns (LBP), Local Phase Quantization (LPQ) and Gabor Filters. Deep learning techniques have been a recent focus of research for classification problems, with state-of-the art results for object recognition and other tasks, but are not yet widely used for texture problems. This paper investigates the usage of deep learning techniques, in particular Convolutional Neural Networks (CNN), for texture classification in two forest species datasets - one with macroscopic images and another with microscopic images. Given the higher resolution images of these problems, we present a method that is able to cope with the high-resolution texture images so as to achieve high accuracy and avoid the burden of training and defining an architecture with a large number of free parameters. On the first dataset, the proposed CNN-based method achieves 95.77% of accuracy, compared to state-of-the-art of 97.77%. On the dataset of microscopic images, it achieves 97.32%, beating the best published result of 93.2%. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICPR.2014.199 | Pattern Recognition |
Keywords | Field | DocType |
forestry,image classification,image resolution,image texture,learning (artificial intelligence),neural nets,object recognition,CNN-based method,Gabor filters,LBP,LPQ,deep convolutional neural networks,forest species recognition,high resolution images,local binary patterns,local phase quantization,macroscopic images,microscopic images,object recognition,texture classification problem | Computer vision,Pattern recognition,Image texture,Computer science,Convolutional neural network,Local binary patterns,Support vector machine,Feature extraction,Artificial intelligence,Deep learning,Image resolution,Cognitive neuroscience of visual object recognition | Conference |
ISSN | Citations | PageRank |
1051-4651 | 20 | 0.82 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luiz G. Hafemann | 1 | 63 | 3.03 |
| Luiz S. Oliveira | 2 | 20 | 0.82 |
| Paulo Rodrigo Cavalin | 3 | 20 | 0.82 |