Improved Dictionary Learning with Enriched Information for Biomedical Images

Shengda Luo, Alex Po Leung; Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2018, pp. 0-0

With dictionary learning using k-means or k-means++, the optimal value of k is traditionally determined empirically using a validation set. The optimal k, which should depend on the particular problem, is chosen with previously determined values from prior work. We argue that there is rich information from clustering with a number of values of k. We propose a novel method to extract information from clustering with all reasonable values of k at the same time. It is shown that our method improves the performance of dictionary learning for the popular bag-of-features model in image classification with simple patterns like cells such as biomedical images. Our experiments demonstrate that, our proposed dictionary learning method outperforms popular methods, on two well-known datasets by 12.5% and 8.5% compared to k-means/kmeans++ dictionary learning and by 8.9% and 6.1% compared to sparse coding.