TY - JOUR
T1 - Feature learning-based segmentation algorithm for hand segmentation
AU - Yang, Shih Hung
AU - Tseng, Yu Min
AU - Chen, Yon Ping
N1 - Funding Information:
This work was supported by the National Science Council and the Ministry of Science and Technology of the Republic of China (Contract No.: NSC 100 - 2410 - H - 035 – 041-and MOST 103-2218-E-035-014-).
Publisher Copyright:
© 2021, MUK Publications and Distribution. All rights reserved.
PY - 2021/6
Y1 - 2021/6
N2 - Feature learning algorithms have been studied extensively for solving many pattern recognition problems, and several effective algorithms have been proposed. This paper proposes a feature learning-based segmentation algorithm (FLSA) for determining appropriate features for hand segmentation. This new approach combines an unsupervised learning phase and a supervised learning phase for hand segmentation. The unsupervised learning phase consists of preprocessing, patch generation, and filter learning through the K-means algorithm. The supervised learning phase consists of feature extraction, classification learning, pixel classification, and morphological operation. The FLSA starts feature learning through the K-means algorithm and then trains a neural network (NN) as the classifier for classifying a pixel into two categories: hand and nonhand. As feature learning progresses, features appropriate for hand segmentation are gradually learned. The emphasis of the FLSA on feature learning can improve the performance of hand segmentation. The Georgia Tech Egocentric Activities data set was used as unlabeled data for feature learning, and the corresponding ground-truth data were used for NN training. The experimental results show that the FLSA can learn features from unlabeled data and verify that feature learning leads to hand segmentation that is more effective than that without feature learning.
AB - Feature learning algorithms have been studied extensively for solving many pattern recognition problems, and several effective algorithms have been proposed. This paper proposes a feature learning-based segmentation algorithm (FLSA) for determining appropriate features for hand segmentation. This new approach combines an unsupervised learning phase and a supervised learning phase for hand segmentation. The unsupervised learning phase consists of preprocessing, patch generation, and filter learning through the K-means algorithm. The supervised learning phase consists of feature extraction, classification learning, pixel classification, and morphological operation. The FLSA starts feature learning through the K-means algorithm and then trains a neural network (NN) as the classifier for classifying a pixel into two categories: hand and nonhand. As feature learning progresses, features appropriate for hand segmentation are gradually learned. The emphasis of the FLSA on feature learning can improve the performance of hand segmentation. The Georgia Tech Egocentric Activities data set was used as unlabeled data for feature learning, and the corresponding ground-truth data were used for NN training. The experimental results show that the FLSA can learn features from unlabeled data and verify that feature learning leads to hand segmentation that is more effective than that without feature learning.
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M3 - Article
AN - SCOPUS:85097144499
VL - 13
SP - 21
EP - 37
JO - International Journal of Computational Intelligence in Control
JF - International Journal of Computational Intelligence in Control
SN - 0974-8571
IS - 1
ER -