This paper presents a scene categorization method that is invariant to affine transformations. We propose a new moment-based normalization algorithm to generate an output image that is independent of the position, rotation, shear, and scale of the input image. In the proposed approach, an affine transform matrix is determined subject to the normalized image satisfying a set of moment constraints. After image normalization, a dense set of local features is extracted using scattering transform, and the global features are then formed via a sparse coding method. We evaluate the proposed method and other state-of-the-art algorithms on a benchmark dataset. The experimental results show that for images distorted with affine transformations, the proposed normalization increases the classification rate by about 28%, compared with the scene categorization approach that uses no normalization.
X. Wei, S. Lam. Phung & A. Bouzerdoum, "Affine-invariant scene categorization," in IEEE International Conference on Image Processing, 2014, pp. 1031-1035.