What is the Spatial Extent of an Object?
This paper poses the question: Can we really achieve a gain in the recognition of objects using its spatial context? We start from Bag-of-Words models and use the Pascal 2007 dataset. We use the rough object bounding boxes that come with the dataset to investigate the fundamental gain context can bring. Our main contributions are: (I) The result of Zhang et al. in CVPR07 that context is superfluous derived from the Pascal 2005 data set of 4 classes does not generalize to this dataset. For our larger and more realistic dataset, context is important indeed. (II) Using the rough bounding box to limit or extend the scope of an object during both training and testing, we find that the spatial extent of an object is determined by its category: (a) well defined, rigid objects have the object itself as the preferred spatial extent. (b) Non-rigid objects have an unbounded spatial extent: all spatial extents produce equally good results. (c) Objects primarily categorised based on their function have the whole image as their spatial extent. Finally, (III) using the rough bounding box to treat object and context separately, we found that the upper bound of improvement is 26% (12% absolute) in terms of Mean Average Precision, and this bound is likely to be higher if the localisation is done using segmentation. It is concluded that object localisation, if done precise, helps considerably in the recognition of objects.