Sparse Coding with Fast Image Alignment via Large Displacement Optical Flow
This addresses alignment issues in image classification for applications like digit recognition, but it is incremental as it builds on existing sparse coding methods.
The paper tackles the performance degradation of sparse representation-based classifiers when training and test images are misaligned, introducing a novel sparse coding framework that adapts dictionary atoms to test images via large displacement optical flow, achieving verified efficacy and robustness on digit datasets.
Sparse representation-based classifiers have shown outstanding accuracy and robustness in image classification tasks even with the presence of intense noise and occlusion. However, it has been discovered that the performance degrades significantly either when test image is not aligned with the dictionary atoms or the dictionary atoms themselves are not aligned with each other, in which cases the sparse linear representation assumption fails. In this paper, having both training and test images misaligned, we introduce a novel sparse coding framework that is able to efficiently adapt the dictionary atoms to the test image via large displacement optical flow. In the proposed algorithm, every dictionary atom is automatically aligned with the input image and the sparse code is then recovered using the adapted dictionary atoms. A corresponding supervised dictionary learning algorithm is also developed for the proposed framework. Experimental results on digit datasets recognition verify the efficacy and robustness of the proposed algorithm.