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Proposed Multi-label Image Classification Method Based on Gabor Filter

  • Ziad AbdallahEmail author
  • Ali El-Zaart
  • Mohamad Oueidat
Conference paper

Abstract

Image Multi-label Classification (IMC) assigns a label or a set of labels to an image. The big demand for image annotation and archiving in the web attracts researchers to develop many algorithms for this application domain. The Multi-Instance Multi-Label Learning (MIML) is an important type of machine learning framework proposed recently for IMC. The drawbacks of the existing methods that they did not take into consideration are: (a) the description of the elementary characteristics from the image, (b) the correlation between labels. In this chapter, we propose a novel algorithm (MIML-GABORLPP), which handles these limitations. The algorithm uses Gabor filter bank as feature descriptor to handle the first limitation. It applies the Label Priority Power set as multi-label transformation to solve the problem of label correlation. The experimental work shows that the results of MIML-GABORLPP are better when compared to two other existing techniques.

Keywords

Data mining Information retrieval system Multi-label Image classification Gabor filter 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mathematics and Computer SciencesBeirut Arab UniversityBeirutLebanon
  2. 2.Maintenance and Industrial Engineering DepartmentUniversity Institute of TechnologySaidaLebanon

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