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We present a novel approach for retrieval of object categories based on a novel type of image representation: the generalized correlogram (GC). In our image representation, the object is described as a constellation of GCs, where each one encodes information about some local part and the spatial relations from this part to others (that is, the part's context). We show how such a representation can be used with fast procedures that learn the object category with weak supervision and efficiently match the model of the object against large collections of images. In the learning stage, we show that, by integrating our representation with Boosting, the system is able to obtain a compact model that is represented by very few features, where each feature conveys key properties about the object's parts and their spatial arrangement. In the matching step, we propose direct procedures that exploit our representation for efficiently considering spatial coherence between the matching of local parts. Combined with an appropriate data organization such as inverted files, we show that thousands of images can be evaluated efficiently. The framework has been applied to different standard databases, and we show that our results are favorably compared against state-of-the-art methods in both computational cost and accuracy.