TY - GEN
T1 - Image Problem Classification for Dashboard Cameras
AU - Hnoohom, Narit
AU - Thanapattherakul, Thanchanok
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/4/21
Y1 - 2017/4/21
N2 - This paper aimed to develop a prediction model to classify problems arising in images obtained from dashboard camera video by using machine-learning algorithms. The authors generated a dataset, called the DS dataset, which contained 900 images. The dataset was divided into three groups of problems comprised of lightness problems, a combination of lightness and blur problems, and a combination of lightness and noise problems. In this study, five features on the dataset were utilised, including mean, standard deviation, entropy, histogram, and variance of the images. Classification was performed on 3 machine-learning algorithms, which were Decision Tree, Naïve Bayes and Support Vector Machines on images and partitions of the images. The experimental results showed that decision tree algorithm yielded the best performance in comparison with the two other algorithms, with the optimal prediction model obtaining an accuracy rate of up to 97.88 percent.
AB - This paper aimed to develop a prediction model to classify problems arising in images obtained from dashboard camera video by using machine-learning algorithms. The authors generated a dataset, called the DS dataset, which contained 900 images. The dataset was divided into three groups of problems comprised of lightness problems, a combination of lightness and blur problems, and a combination of lightness and noise problems. In this study, five features on the dataset were utilised, including mean, standard deviation, entropy, histogram, and variance of the images. Classification was performed on 3 machine-learning algorithms, which were Decision Tree, Naïve Bayes and Support Vector Machines on images and partitions of the images. The experimental results showed that decision tree algorithm yielded the best performance in comparison with the two other algorithms, with the optimal prediction model obtaining an accuracy rate of up to 97.88 percent.
KW - Dashboard camera image
KW - feature extraction
KW - machine learning algorithms
UR - https://www.scopus.com/pages/publications/85019207984
U2 - 10.1109/SITIS.2016.112
DO - 10.1109/SITIS.2016.112
M3 - Conference contribution
AN - SCOPUS:85019207984
T3 - Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016
SP - 673
EP - 678
BT - Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016
A2 - De Pietro, Giuseppe
A2 - Dipanda, Albert
A2 - Chbeir, Richard
A2 - Gallo, Luigi
A2 - Yetongnon, Kokou
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016
Y2 - 28 November 2016 through 1 December 2016
ER -