Logo image
Back
A Robotic Vision Model via Xception and Light Gradient Boosting Machine
Conference proceeding   Peer reviewed

A Robotic Vision Model via Xception and Light Gradient Boosting Machine

Fang Hu, Mingfang Huang, Jia Liu, Xingyu Yan and Xiufeng Cheng
2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), pp.1605-1610
Annual Computers, Software, and Applications Conference (COMPSAC), 45th (Madrid, Spain, 07/12/2021–07/16/2021)
09/2021
DOI: https://doi.org/10.1109/COMPSAC51774.2021.00239
Web of Science ID: WOS:000706529000228

Metrics

1 Record Views

Abstract

Feature extraction Image Classification Light Gradient Boosting Machine Robotic Vision Xception Architecture Computational Mathematics Computer Vision Software
Image classification plays a significant role in robotic vision. This paper proposes an image classification model: Xception-LightGBM, which combines with Xception and Light Gradient Boosting Machine for hybrid image classification. The proposed algorithm produces the image feature extraction via Xception and classifies these feature vectors using Light Gradient Boosting Machine (LightGBM). The Xception-LightGBM model is compared with five representative image prediction models, such as VGG16, VGG19, InceptionV3, DenseNet121, and Xception. The experiments on six data sets demonstrate this proposed model leads to successful runs and provides optimal performances. It shows this model achieves the best results for all six evaluation metrics: accuracy, precision, recall, F1-Score, loss, and Jaccard. Furthermore, this proposed model acquires the highest accuracy on six image data sets, which has at least 1.1% in accuracy improved to the Xception architecture. It suggests this model may be preferable for robotic vision.

Details

Logo image