On object classification: Artificial vs. natural

Recently semantic classification of images is of great interest for image indexing applications. On the one hand, researchers in the field of content-based image retrieval are interested in object(s) of interest in an image, which is useful for representing the image. In this paper, we present a semantic classification method of the object(s) of interest into artificial/natural classes. We first show that dominant orientation features in Gabor filtering results of artificial objects are very useful for discriminating them from natural objects. Dominant orientations in artificial object images are not confined to horizontal and/or vertical directions, while those in artificial scene images tend to be greatly confined to them. Two classification measures are proposed; the sum of sector power differences in Fourier power spectrum and the energy of edge direction histogram. They show classification accuracy of 85.8% and 84.8% on a test with 2,600 object images, respectively.