Robust Scene Categorization by Learning Image Statistics in Context

We present a generic and robust approach for scene categorization. A
complex scene is described by proto-concepts like vegetation, water,
fire, sky etc. These proto-concepts are represented by low level
features, where we use natural images statistics to compactly represent
color invariant texture information by a Weibull distribution. We
introduce the notion of contextures which preserve the context of
textures in a visual scene with an occurrence histogram (context) of
similarities to proto-concept descriptors (texture). In contrast to a
codebook approach, we use the similarity to all vocabulary elements to
generalize beyond the code words. Visual descriptors are attained by
combining different types of contexts with different texture
parameters. The visual scene descriptors are generalized to visual
categories by training a support vector machine. We evaluate our
approach on 3 different datasets: 1) 50 categories for the TRECVID
video dataset; 2) the Caltech 101-object images; 3) 89 categories being
the intersection of the Corel photo stock with the Art Explosion photo
stock. Results show that our approach is robust over different
datasets, while maintaining competitive performance.