Classification of wood knots using artificial neural networks with texture and local feature-based image descriptors

Author:

Hwang Sung-Wook1ORCID,Lee Taekyeong1,Kim Hyunbin2,Chung Hyunwoo2,Choi Jong Gyu3,Yeo Hwanmyeong123

Affiliation:

1. Research Institute of Agriculture and Life Sciences, Seoul National University , 1 Gwanak-ro , Gwanak-gu , Seoul 08826 , Republic of Korea

2. Department of Agriculture, Forestry and Bioresources , College of Agriculture and Life Sciences, Seoul National University , 1 Gwanak-ro , Gwanak-gu , Seoul 08826 , Republic of Korea

3. Department of Forest Sciences , College of Agriculture and Life Sciences, Seoul National University , 1 Gwanak-ro , Gwanak-gu , Seoul 08826 , Republic of Korea

Abstract

Abstract This paper describes feature-based techniques for wood knot classification. For automated classification of macroscopic wood knot images, models were established using artificial neural networks with texture and local feature descriptors, and the performances of feature extraction algorithms were compared. Classification models trained with texture descriptors, gray-level co-occurrence matrix and local binary pattern, achieved better performance than those trained with local feature descriptors, scale-invariant feature transform and dense scale-invariant feature transform. Hence, it was confirmed that wood knot classification was more appropriate for texture classification rather than an approach based on morphological classification. The gray-level co-occurrence matrix produced the highest F1 score despite representing images with relatively low-dimensional feature vectors. The scale-invariant feature transform algorithm could not detect a sufficient number of features from the knot images; hence, the histogram of oriented gradients and dense scale-invariant feature transform algorithms that describe the entire image were better for wood knot classification. The artificial neural network model provided better classification performance than the support vector machine and k-nearest neighbor models, which suggests the suitability of the nonlinear classification model for wood knot classification.

Publisher

Walter de Gruyter GmbH

Subject

Biomaterials

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