Earticle

초록

영어

This paper describes a visual object detection approach utilizing ensemble based machine learning. Object detection methods employing 1D features have the benefit of fast calculation speed. However, for real image with complex background, detection accuracy and performance are degraded. In this paper, we propose an ensemble learning algorithm that combines a 1D feature classifier and 2D DNF (Disjunctive Normal Form) classifier to improve the object detection performance in a single input image. Also, to improve the computing efficiency and accuracy, we propose a feature selecting method to reduce the computing time and ensemble algorithm by combining the 1D features and 2D DNF features. In the verification experiments, we selected the Haar-like feature as the 1D image descriptor, and demonstrated the performance of the algorithm on a few datasets such as face and vehicle.

목차

Abstract
1. Introduction
2. Definition of weak classifier
2.1 Adaboost
2.2 DNF cell classifier
3. Methods for selecting feature and constructing ensembles
3.1 Selecting 2D DNF features
3.2 Building the strong classifier
4. Experiments and results
4.1 Feature extraction
4.2 Experimental results
5. Conclusion
Acknowledgement
References

키워드

저자

• Hyeon-Gyu Min [ Dept. Mechanical Engineering, Pusan National University, Korea ]
• Dong-Joong Kang [ Dept. Mechanical Engineering, Pusan National University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380