Scale invariant texture classification via sparse representation
The scaling property of textures is intensively explored for the first time. By constructing a multi-scale dictionary from a limited number of training images at any scale, the scale invariance could be achieved through a modified sparse representation based classification method. Neither the scale invariant feature extraction nor a large set of labeled images with different scales for training were needed. The proposed method achieved comparative results to the state-of-the-art approaches for texture classification with numerous scale change. The framework might also be applied in other computer vision tasks such as object matching or classification which involves scale change.
scale invariant texture classification sparse representation multi-scale