Earticle

초록

영어

It is the important that Support Vector Machine (SVM) is the powerful learning machines and has been applied to varying task with generally acceptable performance. The SVM success for classification tasks in one domain is affected by features that it represents the instance of specific class. The representative and discriminative features that they are given, SVM learning is going to provide better generalization and consequently that we are able to obtain good classifier. In this paper, we define the problem of feature choices for tasks of human detections and measure the performance of each feature. And also we consider HOG-family feature to study an effective feature selection method. Finally we proposed the multi-scale HOG as a NEW family member in this feature group. In addition we also combine SVM with Principal Component Analysis (PCA) to reduce dimension of features and enhance the evaluation speed while retaining most of discriminative feature vectors.

목차

Abstract
 1. Introduction
 2. Features and the Classifier
  2.1. Second-order Headings
  2.2. Building HOG from Image
  2.3. Support Vector Machine
  2.4. SVM Evaluation with PCA
 3. Experiments
  3.1. Experimental Results
 4. Discussion
 5. Conclusion and Future Direction
 Acknowledgements
 References

키워드

저자

• Kitae Bae [ Department of New Media, Korean German Institute of Technology 661, Deungchon-Dong, Gangseo-Gu, Seoul, 157-033, Republic of Korea ]
• Libor Mesicek [ Department of Informatics, J. E. Purkinje University, Faculty of Science, Ceske mladeze 8, Usti nad Labem, Czech Republic, 400-96 ]
• Hoon Ko [ Department of Informatics, J. E. Purkinje University, Faculty of Science, Ceske mladeze 8, Usti nad Labem, Czech Republic, 400-96 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605