Earticle

초록

영어

We focus on the issue of dataset generation for fine-grained vehicle classification with CNN. Traditionally, to build a large dataset, images must be first collected manually, and then be annotated with a lot of effort. All these work are time-consuming and cost-prohibitive. In this work we propose a novel method that can generate massive images automatically, and these generated images need no annotation. An AutoCAD 3D model of a car of specified make and model is imported into our system, and then images of different views of the car are generated, these images can describe all the details of a car. By taking these images as training dataset, we use a Convolutional Neural Network to train a model for fine-grained vehicle classification. Experimental results show that these images generated virtually by 3D model indeed work as effective as real images.

목차

Abstract
 1. Introduction
 2. Dataset Generating Method
 3. Experimental Results
  3.1. CNN Architecture
  3.2. Experiment Design
  3.3. Discussion
 4. Conclusion
 Acknowledgments
 References

키워드

저자

• Shaoyong Yu [ Department of Cognitive Science, Xiamen University, Xiamen 361000, China, School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China ]
• Zhijun Song [ The 28th Research Institute of China Electronics Technology Group Corporation, Nanjing 210007, China ]
• Songzhi Su [ Department of Cognitive Science, Xiamen University, Xiamen 361000, China ]
• Wei Li [ School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China ]
• Yun Wu [ School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China ]
• Wenhua Zeng [ Department of Cognitive Science, Xiamen University, Xiamen 361000, China ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605