TY - GEN
T1 - Hierarchical Spatial Pyramid Pooling for Fine-Grained Vehicle Classification
AU - Rachmadi, Reza Fuad
AU - Uchimura, Keiichi
AU - Koutaki, Gou
AU - Ogata, Kohichi
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/24
Y1 - 2018/9/24
N2 - In this paper, we proposed a hierarchical spatial pyramid pooling mechanism for improving fine-grained vehicle classification problems. Our proposed method created by removing the last layer of convolutional neural network (CNN) classifier, attaching the hierarchical spatial pyramid pooling at the end of the network, and fine-tune the CNN classifier. Our hierarchical spatial pyramid pooling consists of two independent spatial pyramid pooling that pooled the features from two different layers in the original CNN classifier. We modified ResNet50 and AlexNet CNN classifier using our proposed method and test it on fine-grained vehicle classification dataset. Unlike part-based classifier, which very popular method to tackle fine-grained classification problems, our method does not require any pre-processing or post-processing step in order to do the classification tasks. Experiments on BoxCars fine-grained vehicle classification dataset shows that our proposed method can increase the overall accuracy of the classifier and some of the results achieve state-of-the-art performance.
AB - In this paper, we proposed a hierarchical spatial pyramid pooling mechanism for improving fine-grained vehicle classification problems. Our proposed method created by removing the last layer of convolutional neural network (CNN) classifier, attaching the hierarchical spatial pyramid pooling at the end of the network, and fine-tune the CNN classifier. Our hierarchical spatial pyramid pooling consists of two independent spatial pyramid pooling that pooled the features from two different layers in the original CNN classifier. We modified ResNet50 and AlexNet CNN classifier using our proposed method and test it on fine-grained vehicle classification dataset. Unlike part-based classifier, which very popular method to tackle fine-grained classification problems, our method does not require any pre-processing or post-processing step in order to do the classification tasks. Experiments on BoxCars fine-grained vehicle classification dataset shows that our proposed method can increase the overall accuracy of the classifier and some of the results achieve state-of-the-art performance.
UR - http://www.scopus.com/inward/record.url?scp=85055487890&partnerID=8YFLogxK
U2 - 10.1109/IWBIS.2018.8471695
DO - 10.1109/IWBIS.2018.8471695
M3 - Conference contribution
AN - SCOPUS:85055487890
T3 - 2018 International Workshop on Big Data and Information Security, IWBIS 2018
SP - 19
EP - 24
BT - 2018 International Workshop on Big Data and Information Security, IWBIS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Workshop on Big Data and Information Security, IWBIS 2018
Y2 - 12 May 2018 through 13 May 2018
ER -