AL-DDCNN : a distributed crossing semantic gap learning for person re-identification

By the reason of the variability of light and pedestrians’ appearance, it is hard for a camera to obtain a clear human figure. Person re-identification with different cameras is a difficult visual recognition task.

In this paper, a novel approach called attribute learning based on distributed deep convolutional neural network model is proposed to address person re-identification task. It shows how attributes, namely the mid-level medium between classes and features, are obtained automatically and how they are employed to re-identify person with semantics when an author-topic model is used to mapping category. Besides, considering the ability to operate on raw pixel input without the need to design special features, deep convolutional neural network is employed to generate features without supervision for attributes learning model. To overcome the model’s weakness in computing speed, parallelized implementations such as distributed parameter manipulation and attributes learning are employed in attribute learning based on distributed deep convolutional neural network model.

Experiments show that the proposed approach achieves state-of-the-art recognition performance in the VIPeR data set and is with a good semantic explanation.