Class consistent multi-modal fusion with binary features

Ashish Shrivastava, Mohammad Rastegari, Sumit Shekhar, Rama Chellappa, Larry S. Davis
2015 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
Many existing recognition algorithms combine different modalities based on training accuracy but do not consider the possibility of noise at test time. We describe an algorithm that perturbs test features so that all modalities predict the same class. We enforce this perturbation to be as small as possible via a quadratic program (QP) for continuous features, and a mixed integer program (MIP) for binary features. To efficiently solve the MIP, we provide a greedy algorithm and empirically show
more » ... at its solution is very close to that of a state-of-the-art MIP solver. We evaluate our algorithm on several datasets and show that the method outperforms existing approaches.
doi:10.1109/cvpr.2015.7298841 dblp:conf/cvpr/ShrivastavaRSCD15 fatcat:xyojwbucmvevdimjd7o6o77y7e