Class-Specific Binary Correlograms for Object Recognition

This paper presents an efficient object-class recognition approach based on a new type of image descriptor: the Class-Specific Binary Correlogram (CSBC). In our representation, the image is described by a collection of CSBCs, where each one encodes the spatial distribution of class-specific features around a particular reference point. This representation is obtained by first performing an automatic selection of class-specific features from a vocabulary, and then extracting collections of binary correlograms that encode, at the same time, detected object parts and their spatial distribution around multiple points of the image. Our descriptors live in high-dimensional spaces (in the order of 10K dimensions), but they are very sparse. We show that efficient learning and matching procedures can be obtained for such a representation if we use, first, fast feature selection techniques specific for binary features, and then Boosting integrated with an appropriate Inverted File data organization. The proposed strategy works with weak supervision, outperforms state-of-the-art bag-of-feature methods, and it is more accurate and computationally more efficient than well-known geometrical-based methods, including our previous work on Generalized Correlograms (GCs) [1].