Person re-identification plays an important role in intelligent surveillance analysis, in which it is critical to design a robust feature representation. In this paper, a novel feature representation named Aligned Bidirectional Maximum Occurrence (ABMO) is proposed. First, in order to handle background interference and spatial misalignment, Multi-layer Cellular Automata (MCA) is introduced for foreground segmentation and the alignment operation is put forward. Then, the bidirectional maximum occurrence feature representation is presented, which enhances representation completeness and robustness to illumination variations and viewpoint changes. Our approach is evaluated on an existing re-id dataset (PRID450s) as well as a new, more realistic re-id dataset (PRID365s), where the original surveillance materials are available. The results show that the proposed approach can obtain better re-identification performance compared with other state-of-the-art methods.