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Proceedings Paper

Active dictionary learning for image representation
Author(s): Tong Wu; Anand D. Sarwate; Waheed U. Bajwa
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Paper 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.

Paper Details

Date Published: 22 May 2015
PDF: 10 pages
Proc. SPIE 9468, Unmanned Systems Technology XVII, 946809 (22 May 2015); doi: 10.1117/12.2180018
Show Author Affiliations
Tong Wu, Rutgers, The State Univ. of New Jersey (United States)
Anand D. Sarwate, Rutgers, The State Univ. of New Jersey (United States)
Waheed U. Bajwa, Rutgers, The State Univ. of New Jersey (United States)


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

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