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 coding’s 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.

Recently, sparse coding has become popular for image classification. However, images are often captured under different conditions such as varied poses, scales and different camera parameters. This means local features may not be discriminative enough to cope with these variations. To solve this problem, affine transformation along with sparse coding is proposed. Although proven effective, the affine sparse coding has no constraints on the tilt and orientations as well as the encoding parameter consistency of the transformed local features. To solve these problems, we propose a Laplacian affine sparse coding algorithm which combines the tilt and orientations of affine local features as well as the dependency among local features. We add tilt and orientation smooth constraints into the objective function of sparse coding. Besides, a Laplacian regularization term is also used to characterize the encoding parameter similarity. Experimental results on several public datasets demonstrate the effectiveness of the proposed method.