Object and scene image classification using unconventional color descriptors

Document Type

Conference Proceeding

Publication Date

12-1-2012

Abstract

This paper presents novel color, texture and shape descriptors for scene and object image classification and evaluates their performance in unconventional color spaces. First, a new three dimensional Local Binary Pattern (3D-LBP) descriptor is proposed for color and texture feature extraction. Second, a novel color HWML (HOG of Wavelet of Multiplanar LBP) descriptor is derived by computing the histogram of the orientation gradients (HOG) of the Haar wavelet transformation of the original image and the 3D-LBP images. Third, these descriptors are generated in the unconventional color spaces like oRGB, I1I2I3, uncorrelated and discriminating color spaces to improve performance over conventional color spaces like RGB and HSV. Fourth, the Enhanced Fisher Model (EFM) is applied for discriminatory feature extraction and the nearest neighbor classification rule is used for image classification. Finally, the Caltech 256 object categories database and the MFT scene dataset are used to demonstrate the feasibility of the proposed new methods.

Identifier

84873299586 (Scopus)

ISBN

[9781601322258]

Publication Title

Proceedings of the 2012 International Conference on Image Processing Computer Vision and Pattern Recognition Ipcv 2012

First Page

695

Last Page

701

Volume

2

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