TY - GEN
T1 - Non-negative dictionary learning with pairwise partial similarity constraint
AU - Zhou, Xu
AU - Ding, Pak Lun Kevin
AU - Li, Baoxin
N1 - Funding Information: Acknowledgments: The work was supported in part by grants from ONR and ARO. Any opinions expressed in this material are those of the authors and do not necessarily reflect the views of ONR or ARO. Publisher Copyright: © 2017 IEEE.
PY - 2017/8/28
Y1 - 2017/8/28
N2 - Discriminative dictionary learning has been widely used in many applications such as face retrieval / recognition and image classification, where the labels of the training data are utilized to improve the discriminative power of the learned dictionary. This paper deals with a new problem of learning a dictionary for associating pairs of images in applications such as face image retrieval. Compared with a typical supervised learning task, in this case the labeling information is very limited (e.g. only some training pairs are known to be associated). Further, associated pairs may be considered similar only after excluding certain regions (e.g. sunglasses in a face image). We formulate a dictionary learning problem under these considerations and design an algorithm to solve the problem. We also provide a proof for the convergence of the algorithm. Experiments and results suggest that the proposed method is advantageous over common baselines.
AB - Discriminative dictionary learning has been widely used in many applications such as face retrieval / recognition and image classification, where the labels of the training data are utilized to improve the discriminative power of the learned dictionary. This paper deals with a new problem of learning a dictionary for associating pairs of images in applications such as face image retrieval. Compared with a typical supervised learning task, in this case the labeling information is very limited (e.g. only some training pairs are known to be associated). Further, associated pairs may be considered similar only after excluding certain regions (e.g. sunglasses in a face image). We formulate a dictionary learning problem under these considerations and design an algorithm to solve the problem. We also provide a proof for the convergence of the algorithm. Experiments and results suggest that the proposed method is advantageous over common baselines.
KW - Constrained dictionary learning
KW - Face retrieval
KW - Non-negative
UR - http://www.scopus.com/inward/record.url?scp=85030246689&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85030246689&partnerID=8YFLogxK
U2 - 10.1109/ICME.2017.8019392
DO - 10.1109/ICME.2017.8019392
M3 - Conference contribution
T3 - Proceedings - IEEE International Conference on Multimedia and Expo
SP - 1410
EP - 1415
BT - 2017 IEEE International Conference on Multimedia and Expo, ICME 2017
PB - IEEE Computer Society
T2 - 2017 IEEE International Conference on Multimedia and Expo, ICME 2017
Y2 - 10 July 2017 through 14 July 2017
ER -