Deep adaptive feature embedding with local sample distributions for person re-identification

Highlights：

• An improved deep feature embedding approach for person re-identification is presented to learn representations amenable to similarity score computation.

• The quality of learned representations and the training efficiency is augmented by jointly optimizing robust feature embedding, local adaptive similarity learning, and suitable positive mining.

• An alternative to CNN embedding is presented by formulating a stacked CRBMs into local sample structure in deep feature space, and thus enables local adaptive similarity metric learning as well as plausible positive mining.

• Stochastic gradient descent is modified to reuse past computed gradients from neighborhood data points, leading to linear convergence.