Scene classification using multi-resolution low-level feature combination

Most of the current scene classification approaches are based on either low-level or semantic modeling strategies. However, the both strategies have some inherent weaknesses. The low-level modeling based approaches normally classify images into a small number of scene categories and often exhibit poor performance. And the semantic modeling strategies usually bring high computational cost and memory consumption. In this paper, we present a novel approach which retains the advantages of both the low-level and semantic modeling strategies, while at the same time getting over the weaknesses of these two strategies. To represent scene images more effectively, a new visual descriptor called GBPWHGO is introduced. Experimental results on six commonly used data sets demonstrate that our approach performs competitively against previous methods across all data sets, and the GBPWHGO descriptor outperforms the SIFT, LBP and Gist descriptors in scene classification.