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Visual Comparison Based on Multi-class Classification Model

  • Hanqin Shi
  • Liang Tao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10749)

Abstract

Visual comparison is that given two images, we can not only predict which one exhibits a particular visual attribute more than the other, but also predict whether a visual attribute of one image is equal to that of another image. Most existing methods for visual comparison relying on ranking Support Vector Machine (SVM) functions only distinguish which image in a pair exhibits an attribute more or less in test time. However, it is significant to distinguish which image in a pair exhibits an attribute more, less or equal in test time. To address this issue, we propose a multi-class classification model based on one-versus-one method for visual comparison, which can be formulated by learning mapping functions between any two different classes in image pairs. With regard to the mapping functions, we choose the linear regression functions. Experimental results on the three databases of UT-Zap50K-1, OSR and PubFig demonstrate the advantages of the proposed method.

Keywords

Visual comparison Multi-class classification Linear regression model Ranking SVM Relative attributes 

Notes

Acknowledgement

This study was funded by information assurance technology collaborative innovation center of anhui university.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyAnhui UniversityHefeiChina
  2. 2.School of Computer Science and TechnologyHuaibei Normal UniversityHuaibeiChina

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