Learning mixed templates for object recognition

This article proposes a method for learning object templates composed of local sketches and local textures, and investigates the relative importance of the sketches and textures for different object categories. Local sketches and local textures in the object templates account for shapes and appearances respectively. Both local sketches and local textures are extracted from the maps of Gabor filter responses. The local sketches are captured by the local maxima of Gabor responses, where the local maximum pooling accounts for shape deformations in objects. The local textures are captured by the local averages of Gabor filter responses, where the local average pooling extracts texture information for appearances. The selection of local sketch variables and local texture variables can be accomplished by a projection pursuit type of learning process, where both types of variables can be compared and merged within a common framework. The learning process returns a generative model for image intensities from a relatively small number of training images. The recognition or classification by template matching can then be based on log-likelihood ratio scores. We apply the learning method to a variety of object and texture categories. The results show that both the sketches and textures are useful for classification, and they complement each other.