Gabor-based novel color descriptors for object and scene image classification

Document Type

Conference Proceeding

Publication Date

12-1-2012

Abstract

This paper presents several novel Gabor-based color descriptors for object and scene image classification. Firstly, a new Gabor-HOG descriptor is proposed for image feature extraction. Secondly, the Gabor-LBP descriptor derived by concatenating the Local Binary Patterns (LBP) histograms of all the component images produced by applying Gabor filters is integrated with the Gabor-HOG using an optimal feature representation method to introduce a novel Gabor-LBP-HOG (GLH) image descriptor which performs well on different object and scene image categories. Finally, 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 GLH feature vector is evaluated using three grand challenge datasets, namely the Caltech 256 dataset, the MTT Scene dataset and the UIUC Sports Event dataset.

Identifier

84873322860 (Scopus)

ISBN

[9781601322258]

Publication Title

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

First Page

1142

Last Page

1148

Volume

2

This document is currently not available here.

Share

COinS