THE FOURIER TRANSFORM BASED DESCRIPTOR FOR VISUAL OBJECT CLASSIFICATION

Most of the state-of-arts visual object classification methods use image representations such as bag of words (BoW) or Fisher vector (FV) models, which are built depend on encoding local features. In that context, local patches sampled from images are represented by different shape and texture descriptors such as SIFT, LBP, SURF, etc. In this study, we define a new descriptor depend on weighted histograms of phase angles of local 2-D discrete Fourier transform (FT). We make comparison with the classification accuracies achieved by using the proposed descriptor to the ones obtained by other commonly used descriptors on Caltech 4, Caltech-101, Coil-100 and PASCAL VOC 2007 data sets. Experimental results show that our proposed descriptor provides good accuracies (the best results on Caltech-4 and Coil-100, and the second best result on Caltech-101 and PASCAL VOC 2007 datasets) reporting that FT based local descriptor obtain major belongings of images that are valuable for visual object classification. The combination of image representations resulting from FT descriptor with the representations is achieved by other descriptors, results even get better put forwarding that tested descriptors encode different supplementary knowledge.