Local autocorrelation of similarities with subspaces for shift invariant scene classification

This paper presents a shift invariant scene classification method based on local autocorrelation of similarities with subspaces. Although conventional scene classification methods used bag-of-visual words for scene classification, superior accuracy of kernel principal component analysis (KPCA) of visual words to bag-of-visual words was reported. Here we also use KPCA of visual words to extract rich information for classification. In the original KPCA of visual words, all local parts mapped into subspace were integrated by summation to be robust to the order, the number, and the shift of local parts. This approach discarded the effective properties for scene classification such as the relation with neighboring regions. To use them, we use (normalized) local autocorrelation (LAC) feature of the similarities with subspaces (outputs of KPCA of visual words). The feature has both the relation with neighboring regions and the robustness to shift of objects in scenes. The proposed method is compared with conventional scene classification methods using the same database and protocol, and we demonstrate the effectiveness of the proposed method.