Gabor-based novel local, shape and color features for image classification

Atreyee Sinha, Sugata Banerji, Chengjun Liu

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

2 Scopus citations


This paper introduces several novel Gabor-based local, shape and color features for image classification. First, a new Gabor-HOG (GHOG) descriptor is proposed for image feature extraction by concatenating the Histograms of Oriented Gradients (HOG) of all the local Gabor filtered images. The GHOG descriptor is then further assessed in six different color spaces to measure classification performance. Finally, a novel Fused Color GHOG (FC-GHOG) feature is presented by integrating the PCA features of the six color GHOG descriptors that performs well on different object and scene image categories. The Enhanced Fisher Model (EFM) is applied for discriminatory feature extraction and the nearest neighbor classification rule is used for image classification. The robustness of the proposed GHOG and FC-GHOG feature vectors is evaluated using two grand challenge datasets, namely the Caltech 256 dataset and the MIT Scene dataset.

Original languageEnglish (US)
Title of host publicationNeural Information Processing - 19th International Conference, ICONIP 2012, Proceedings
Number of pages8
EditionPART 3
StatePublished - 2012
Externally publishedYes
Event19th International Conference on Neural Information Processing, ICONIP 2012 - Doha, Qatar
Duration: Nov 12 2012Nov 15 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume7665 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other19th International Conference on Neural Information Processing, ICONIP 2012

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


  • Color spaces
  • Enhanced Fisher Model (EFM)
  • Fused Color GHOG (FC-GHOG) descriptor
  • Gabor filters
  • Histograms of Oriented Gradients (HOG)
  • Image search
  • Principal Component Analysis (PCA)
  • The Gabor-HOG (GHOG) descriptor


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