Ensembles of gradient based descriptors with derivative filters for visual object categorization

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper describes several ensemble methods that combine multiple edge and orientation based histograms with support vector machine classifiers. The aim is to enhance learning speed and accuracy performance by using the chosen classical primitive filters on different edge and orientation descriptors. For efficiently describe images using these descriptors, the combination of a few basis features or edge filters are used. The stronger filter operator responds to edge-like structures, the more sensitive it to orientation. Thus, using more than one edge filter allows to capture more edge information to completely describe the structure of image content. One problem in combining these different descriptors is that the input vector becomes very large dimensionality, which can increase problems of overfitting and hinder generalization performance. The intuitively designed ensemble methods namely product, mean and majority are then used to combine support vector machines classifiers derived from the multiple orientations of edge operators. The results indicate that the ensemble methods outperform the single and naive classifiers.

Original languageEnglish
Title of host publicationAdvances in Intelligent Systems and Computing
Pages33-43
Number of pages11
Volume208 AISC
DOIs
Publication statusPublished - 2013
Event1st International Conference on Robot Intelligence Technology and Applications, RiTA 2012 - Gwangju
Duration: 16 Dec 201218 Dec 2012

Publication series

NameAdvances in Intelligent Systems and Computing
Volume208 AISC
ISSN (Print)21945357

Other

Other1st International Conference on Robot Intelligence Technology and Applications, RiTA 2012
CityGwangju
Period16/12/1218/12/12

Fingerprint

Classifiers
Derivatives
Support vector machines

Keywords

  • compass filters
  • ensemble rules
  • object categorization
  • support vector machines

ASJC Scopus subject areas

  • Computer Science(all)
  • Control and Systems Engineering

Cite this

Eqlouss, E. A. A., Abdullah, A., & Sheikh Abdullah, S. N. H. (2013). Ensembles of gradient based descriptors with derivative filters for visual object categorization. In Advances in Intelligent Systems and Computing (Vol. 208 AISC, pp. 33-43). (Advances in Intelligent Systems and Computing; Vol. 208 AISC). https://doi.org/10.1007/978-3-642-37374-9_4

Ensembles of gradient based descriptors with derivative filters for visual object categorization. / Eqlouss, Enas A A; Abdullah, Azizi; Sheikh Abdullah, Siti Norul Huda.

Advances in Intelligent Systems and Computing. Vol. 208 AISC 2013. p. 33-43 (Advances in Intelligent Systems and Computing; Vol. 208 AISC).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Eqlouss, EAA, Abdullah, A & Sheikh Abdullah, SNH 2013, Ensembles of gradient based descriptors with derivative filters for visual object categorization. in Advances in Intelligent Systems and Computing. vol. 208 AISC, Advances in Intelligent Systems and Computing, vol. 208 AISC, pp. 33-43, 1st International Conference on Robot Intelligence Technology and Applications, RiTA 2012, Gwangju, 16/12/12. https://doi.org/10.1007/978-3-642-37374-9_4
Eqlouss EAA, Abdullah A, Sheikh Abdullah SNH. Ensembles of gradient based descriptors with derivative filters for visual object categorization. In Advances in Intelligent Systems and Computing. Vol. 208 AISC. 2013. p. 33-43. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-642-37374-9_4
Eqlouss, Enas A A ; Abdullah, Azizi ; Sheikh Abdullah, Siti Norul Huda. / Ensembles of gradient based descriptors with derivative filters for visual object categorization. Advances in Intelligent Systems and Computing. Vol. 208 AISC 2013. pp. 33-43 (Advances in Intelligent Systems and Computing).
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