Appearance Based Qualitative Image Description for Object Class Recognition

The problem of recognizing classes of objects as opposed to special instances requires methods of comparing images that capture the variation within the class while they discriminate against objects outside the class. We present a simple method for image description based on histograms of qualitative shape indexes computed from the combination of triplets of sampled locations and gradient directions in the image. We demonstrate that this method indeed is able to capture variation within classes of objects and we apply it to the problem of recognizing four different categories from a large database. Using our descriptor on the whole image, containing varying degrees of background clutter, we obtain results for two of the objects that are superior to the best results published so far for this database. By cropping images manually we demonstrate that our method has a potential to handle also the other objects when supplied with an algorithm for searching the image. We argue that our method, based on qualitative image properties, capture the large range of variation that is typically encountered within an object class. This means that our method can be used on substantially larger patches of images than existing methods based on simpler criteria for evaluating image similarity.