Comparison of invariant descriptors for object recognition

This paper deals with the performance evaluation of three object invariant descriptors: Hu moments, Zernike moments and Fourier-Mellin descriptors. Experiments are conducted on a database of 100 objects extracted from the Columbia Object Image Library (COIL-100). Original images from this database only present geometric transformations. They therefore allow to quantify the scale and rotation invariances of the different features and to compare their ability to discriminate objects. In order to test the robustness of the three tested descriptors, we have completed the data set by images including different perturbations: noise, occlusion, luminance variation, backgrounds (uniform, with noise, textured) added to the object. Recognition tests are realised using a support vector machine as supervised classification method. Experimental results are summarized and analyzed permitting to compare the global performances of the different descriptors.