2015 IEEE International Conference on Computer Vision (ICCV) (2015)
Dec. 7, 2015 to Dec. 13, 2015
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.426
We propose a novel Multi-Task Learning with Low Rank Attribute Embedding (MTL-LORAE) framework for person re-identification. Re-identifications from multiple cameras are regarded as related tasks to exploit shared information to improve re-identification accuracy. Both low level features and semantic/data-driven attributes are utilized. Since attributes are generally correlated, we introduce a low rank attribute embedding into the MTL formulation to embed original binary attributes to a continuous attribute space, where incorrect and incomplete attributes are rectified and recovered to better describe people. The learning objective function consists of a quadratic loss regarding class labels and an attribute embedding error, which is solved by an alternating optimization procedure. Experiments on three person re-identification datasets have demonstrated that MTL-LORAE outperforms existing approaches by a large margin and produces state-of-the-art results.
Cameras, Correlation, Measurement, Linear programming, Semantics, Probes, Computer vision
C. Su, F. Yang, S. Zhang, Q. Tian, L. S. Davis and W. Gao, "Multi-Task Learning with Low Rank Attribute Embedding for Person Re-Identification," 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2015, pp. 3739-3747.