Scene categorization based on local-global feature fusion and multi-scale multi-spatial resolution encoding

With the bag-of-contextual-visual-word (BOCVW) models, we propose a scene categorization method based on local-global feature fusion and multi-scale multi-spatial resolution encoding. First, the performances of the BOCVW models belonging to different features are mutually reinforced by fusing other types of features within local regions. Then, the spatial configuration information is explored using a multi-scale multi-spatial resolution encoding approach. Furthermore, these encoded BOCVW models are globally fused using an improved maximum-margin optimization strategy, which considers the margin between input vectors of different categories and the diameter of the smallest ball containing feature vectors simultaneously. The proposed method has been evaluated on three scene categorization datasets consisting of scene categories 8, 15, and 67, respectively. And its effectiveness has been verified by these experimental results.