Simple object recognition based on spatial relations and visual features represented using irregular pyramids

Spatial relations among objects and object parts play a fundamental role in the human perception and understanding of images, thus becoming very relevant in the computational fields of object recognition, scene understanding and content-based image retrieval. In this work we propose a graph matching scheme that involves color, texture and shape features along with spatial descriptors to represent topological and orientation/directional relationships—which are obtained by means of combinatorial pyramids—in order to identify similar objects from a database. We also suggest a method for deciding which are the more useful levels in the hierarchy of segmentation for the recognition process. Our main objective is to prove that the combination of visual and spatial features is a promising road in order to improve the object recognition task. We performed experiments on two well known databases, COIL-100 and ETH-80 image sets, in order to evaluate the expressiveness of the proposed representation. These sets introduce challenges for simple object recognition in terms of view-point changes, and our results were comparable or superior than other state-of-the-art methods.