Bag-of-features representations using spatial visual vocabularies for object classification

This paper presents a novel method for combining local image features and spatial information for object classification tasks using the Bag-of-Features principle. The feature descriptor is extended by additional spatial information. Hence, similar feature descriptors do not only describe similar image patches, but similar patches in roughly the same region. Different spatial measures are evaluated on the Caltech 101 dataset showing the improvement by incorporating spatial information into the feature descriptor. Furthermore, the method achieves better classification rates than the comparable Spatial Pyramids with lower a dimensional representation.