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We study an approach for learning to detect objects in still gray images, that is based on a spares, part-based representation of objects. A vocabulary of information-rich object parts is automatically constructed from a set of sample images of the object class of interest. Images are then represented using parts from this vocabulary, along with spatial relations observed among them. Based on this representation, a feature-efficient learning algorithm is used to learn to detect instances of the object class. The pramework developed can be applied to any object with distinguishable parts in a relatively fixed spatial configuration. So far we have experimented on images of side views of cars. Our experiments show that the method achieves a high detection accuracy on a difficult test set of real-world images, and is highly robust to partial occlusion and background variation.