Multiresolution Laplacian Sparse Coding Technique for Image Classification

Sparse coding is a set of techniques used for learning a collection of over-complete bases to represent data efficiently. This technique has been used in different domain such as feature quantization and image classification. Despite its capacity of modeling, it could not represent similarity of the image coding which cause a poor performance in locality. The cause of this limitation is the features separation of the representation. To surmount the limitations of these techniques, we propose a new approach that is able to calculate similarity by taking into account the image’s spatial neighborhood of pixels. This approach is based on the integration of Kullback-Leibler distance and wavelet decomposition in the domain of image.  The association of the Kullback-leibler distance and wavelet decomposition is robust to small deformations (translation, dilation and rotation). It improves the representation of locality by considering each element of an image and its neighbors in similarity calculation. Results show clear improvements in performance compared to the above techniques.