Earticle

초록

영어

This paper describes a process of developing commercial real time image recognition system with company. In this paper we will make a system that is combining an empirical kernel map method and conjugate least squares support vector machine in order to represent images in a low-dimensional subspace for real time image recognition. In the traditional approach calculating these eigenspace models, known as traditional PCA method, model must capture all the images needed to build the internal representation. Updating of the existing eigenspace is only possible when all the images must be kept in order to update the eigenspace, requiring a lot of storage capability. Proposed method allows discarding the acquired images immediately after the update. By experimental results we can show that empirical kernel map has similar accuracy compare to traditional batch way eigenspace method and more efficient in memory requirement than traditional one. This experimental result shows that proposed model is suitable for commercial real time image recognition system.

목차

Abstract
 1. Introduction
 2. Incremental PCA
  2.1 Updating Image Representations
 3. Empirical Feature Map
 4. Experiment
  4.1 Toy Data
  4.2 LS-SVM for Large Size Data
  4.3 The KinFaceW-I Face Data Set
  4.4 Comparison with SVM
 5. Conclusion and Remarks
 Acknowledgement
 References

키워드

저자

• Byung Joo Kim [ Department of Computer Engineering, Youngsan University ] 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