Improved Dictionary Learning with Enriched Information for Biomedical Images

Abstract

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/k-means++ dictionary learning and by 8.9 $\%$ and 6.1 $\%$ compared to sparse coding.