Weighted Dissociated Dipoles: An Extended Visual Feature Set

The complexity of any learning task depends on the learning method as on finding a good data representation. In the concrete case of object recognition in computer vision, the representation of the images is one of the most important decisions in the design step. As a starting point, in this work we use the representation based on Haar-like filters, a biological inspired feature set based on local intensity differences, which has been successfully applied to different object recognition tasks, such as pedestrian or face recognition problems. From this commonly used representation, we jump to the dissociated dipoles, another biological plausible representation which also includes non-local comparisons. After analyzing the benefits of both representations, we present a more general representation which brings together all the good properties of Haar-like and dissociated dipoles representations. Since these feature sets cannot be used with the classical Adaboost approach due computational limitations, an evolutionary learning algorithm is used to test them over different state of the art object recognition problems. Besides, an extended statistically study of these results is performed in order to verify the relevance of these huge feature spaces.