Earticle

초록

영어

As a new learning framework, Multi-Instance learning is labeled recently and has successfully found application in vision classification. A novel Multi-instance bag generating method is presented in this paper on basis of Gaussian Mixed Model. The generated GMM model composes not only color but also the locally stable unchangeable components. It is frequently named as MI bag by researchers. Besides, another method called Agglomerative Information Bottleneck clustering is applied to replace the MIL problem with the help of single-instance learning ones. Meanwhile, single-instance classifiers are employed for classification. Finally, ensemble learning is adopted to strengthen classifiers’ generalization ability of RBM (Restricted Boltzmann Machine) as the base classifier. On the basis of large-scale datasets, this method is tested and the corresponding result shows that our method provides high accuracy and good performance for image annotation, feature matching and example-based object-classification.

목차

Abstract
 1. Introduction
 2. Method for Multi-Instance Learning
  2.1. Multi-Instance Bag Generating Via Mixture of Gaussians
  2.2. Conversion of Multi-Instance Problem Based on AIB
  2.3. Classifier Ensemble
 3. Experiment Results
  3.1. Caltech-101 Dataset
  3.2. Local-Patch Indexing
 4. Discussion and Conclusion
 Acknowledgments
 References

키워드

저자

• Liu Xiao-jun [ Department of Logistics and Information Management, Zhuhai College of Jilin University, Zhuhai 519041, Guangdong, China ]
• Li Qing-ling [ Faculty of Business and Administration, University of Macau, Macau 999078, Macau ]
• Li Yong-jian [ Department of Logistics and Information Management, Zhuhai College of Jilin University, Zhuhai 519041, Guangdong, China ]
• Li Jun-yi [ Electrical and Computer Engineering Department, National University of Singapore, Singapore 119077, Singapore ]

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