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.

Date Published: 22 May 2015
PDF: 10 pages
Proc. SPIE 9468, Unmanned Systems Technology XVII, 946809 (22 May 2015); doi: 10.1117/12.2180018

Published in SPIE Proceedings Vol. 9468:
Unmanned Systems Technology XVII
Robert E. Karlsen; Douglas W. Gage; Charles M. Shoemaker; Grant R. Gerhart, Editor(s)