Geometrically invariant color, shape and texture features for object recognition using multiple kernel learning classification approach

Abstract The geometrically invariant image descriptors are very important for recognizing objects of arbitrary shapes and orientations. We propose a framework for the fusion of the geometrically invariant descriptors representing color, shape, and texture for the recognition of color objects using multiple kernel learning (MKL) approach. To describe texture of color images, we propose an effective rotation invariant texture descriptor which is based on the Zernike moments (ZMs) of the gradient of the color images, referred to as the GZMs. For the shape features, we use the ZMs of the intensity component of a color image and also use multi-channel ZMs (MZMs) which have proven to be superior in performance than the quaternion ZMs (QZMs). For comparative performance analysis, rotation invariants of the QZMs (RQZMs) are also considered. Since the color histograms (CH) are known to be very effective color descriptors, we consider them for representing color. The five sets of features – CH, ZMs, GZMs, MZMs, and RQZMs are invariant to translation, rotation, and scale. The fusion of color, shape and texture features in different combinations using the MKL approach is shown to provide very high recognition rates on PASCAL VOC 2005, Soccer, SIMPLIcity, Flower, and Caltech-101 datasets.