22 May 2015 Active dictionary learning for image representation
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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.
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Tong Wu, Tong Wu, Anand D. Sarwate, Anand D. Sarwate, Waheed U. Bajwa, Waheed U. Bajwa, } "Active dictionary learning for image representation", Proc. SPIE 9468, Unmanned Systems Technology XVII, 946809 (22 May 2015); doi: 10.1117/12.2180018; https://doi.org/10.1117/12.2180018

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