Laplacian affine sparse coding with tilt and orientation consistency for image classification
We propose a Laplacian affine sparse coding method with tilt, orientation and dependency. We propose to add tilt and orientation smooth constraints into sparse codings objective function. We use a Laplacian regularization term to characterize affine local features similarity. We achieve the state-of-the-art performance on several public datasets.
sparse coding tilt and orientation image classification smooth constraints affine transformation laplacian matrix object categorization bag-of-visual words model