Sparse Coding with Fast Image Alignment via Large Displacement Optical Flow

TL;DR

This paper introduces a novel sparse coding framework that uses large displacement optical flow to align dictionary atoms with input images, significantly improving classification accuracy under misalignment conditions.

Contribution

It presents a new method combining optical flow with sparse coding to adapt dictionaries to misaligned images, enhancing robustness in image classification.

Findings

Improved accuracy on digit recognition datasets.

Robustness to image misalignment and occlusion.

Effective dictionary adaptation via optical flow.

Abstract

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.