Abstract
Sparse representations of images in overcomplete bases (i.e., redundant dictionaries) have many applications in
computer vision and image processing. Recent works have demonstrated improvements in image representations
by learning a dictionary from training data instead of using a predefined one. But learning a sparsifying dictionary
can be computationally expensive in the case of a massive training set. This paper proposes a new approach,
termed active screening, to overcome this challenge. Active screening sequentially selects subsets of training
samples using a simple heuristic and adds the selected samples to a "learning pool," which is then used to learn
a newer dictionary for improved representation performance. The performance of the proposed active dictionary
learning approach is evaluated through numerical experiments on real-world image data; the results of these
experiments demonstrate the effectiveness of the proposed method.