JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2016, Vol. 46 ›› Issue (6): 15-22.doi: 10.6040/j.issn.1672-3961.1.2016.019

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Neighborhood related multiple-instance classifiers based on integrated Hausdorff distance

CHEN Zehua, SHANG Xiaohui, CHAI Jing   

  1. College of Information Engineering, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China
  • Received:2016-07-12 Online:2016-12-20 Published:2016-07-12

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Abstract: Based on the analysis of minimal Hausdorff(minH)and maximal Hausdorff(maxH)distances, integrated Hausdorff(intH)distance was proposed to combine minH and maxH, and used to design neighborhood related multiple-instance classifiers. The Neighborhood Component Analysis(NCA)model was used to learn the weighting coefficients in intH automatically and obtain the optimal intH according to the neighborhood related classification criterion. The experimental results showed that in most cases, compared with minH and maxH, intH could improve the classification accuracies of neighborhood related multiple-instance classifiers.

Key words: multiple-instance learning, weighting coefficients, Hausdorff distance, classifier, neighborhood component analysis

CLC Number: 

  • TP181
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