An Investigation of Gabor PCA and Different Similarity Measure Techniques for Image Classification

  • N. Hemavathi
  • T. R. Anusha
  • K. Mahantesh
  • V. N. Manjunath Aradhya
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 381)


A method of attributing an image to a particular category from a large collection of images is stated as Image Classification. In this paper, we propose diverse subspace techniques, which concentrate on consistency and orientations of an image, extracting color, shape, and texture data with orthogonal transformation into uncorrelated space. Initially, preprocessing is done by transforming image to HSV color space as it is similar to human color perception property. Later, most informative score features are obtained using PCA, MPCA, KPCA, and GPCA with linear and nonlinear projection onto lower dimensional space which are further classified using diverse similarity measures and neural networks. The performance analysis is carried out on large multi-class datasets such as Corel-1K, Caltech-101, and Caltech-256 and the improvised correctness rate is witnessed in comparison with several benchmarking methods.


Image retrieval PCA MPCA KPCA GPCA G-vectors Score features Similarity measures GRNN PNN 


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Copyright information

© Springer India 2016

Authors and Affiliations

  • N. Hemavathi
    • 1
  • T. R. Anusha
    • 1
  • K. Mahantesh
    • 1
  • V. N. Manjunath Aradhya
    • 2
  1. 1.Department of ECESri Jagadguru Balagangadhara Institute of TechnologyBangaloreIndia
  2. 2.Department of MCASri Jayachamarajendra College of EngineeringMysoreIndia

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