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Person reidentification (re-id), as an important task in video surveillance and forensics applications, has been widely studied. Previous research efforts toward solving the person re-id problem have primarily focused on constructing robust vector description by exploiting appearance's characteristic, or learning discriminative distance metric by labeled vectors. Based on the cognition and identification process of human, we propose a new pattern, which transforms the feature description from characteristic vector to discrepancy matrix. In particular, in order to well identify a person, it converts the distance metric from vector metric to matrix metric, which consists of the intradiscrepancy projection and interdiscrepancy projection parts. We introduce a consistent term and a discriminative term to form the objective function. To solve it efficiently, we utilize a simple gradient-descent method under the alternating optimization process with respect to the two projections. Experimental results on public datasets demonstrate the effectiveness of the proposed pattern as compared with the state-of-the-art approaches.

Image annotation has attracted a lot of research interest, and multi-label learning is an effective technique for image annotation. How to effectively exploit the underlying correlation among labels is a crucial task for multi-label learning. Most existing multi-label learning methods exploit the label correlation only in the output label space, leaving the connection between the label and the features of images untouched. Although, recently some methods attempt toward exploiting the label correlation in the input feature space by using the label information, they cannot effectively conduct the learning process in both the spaces simultaneously, and there still exists much room for improvement. In this paper, we propose a novel multi-label learning approach, named multi-label dictionary learning (MLDL) with label consistency regularization and partial-identical label embedding MLDL, which conducts MLDL and partial-identical label embedding simultaneously. In the input feature space, we incorporate the dictionary learning technique into multi-label learning and design the label consistency regularization term to learn the better representation of features. In the output label space, we design the partial-identical label embedding, in which the samples with exactly same label set can cluster together, and the samples with partial-identical label sets can collaboratively represent each other. Experimental results on the three widely used image datasets, including Corel 5K, IAPR TC12, and ESP Game, demonstrate the effectiveness of the proposed approach.