A fast and robust descriptor for multiple-view object recognition

In this paper we propose a fast and robust descriptor for multiple view object recognition using a small number of training examples. In order to design a descriptor to be discriminative between many different object appearances, we base it on a combination of invariant color, edge and texture descriptors. We use a color descriptor based on a HSV histogram - as it is robust to size and position of the object -, a gray level cooccurrence matrix as texture descriptor and an edge histogram as shape descriptor. After extraction of feature vectors, we perform normalization on all feature vectors from the training database in order to increase the importance of the most dominant feature components and reduce the less dominant ones. This normalization improves the recognition performance with almost 30% in case of a small number of training objects and in case of noise or occlusion. We tested our descriptor on the Columbia Object Image Library dataset (COIL 100) which presents objects in scaled, translated and rotated versions. Our recognition rate is extremely high: 99% in case of a large number of training objects and 93% for training with only 4 views of the object, or 5% of the database. The descriptor was also tested under various distortions: illumination change, noise corruption and occlusions. It proved to be very robust, with recognition rates decreasing only less then 5%. We compared our results with state of the art methods and we conclude that our descriptor achieves a better performance, both on the regular COIL database and on all distorted variants.