We present a novel multi-shot re-identification method, that merges together two different pattern recognition paradigms for describing objects: feature-based and relation-based. The former aims at encoding visual properties that characterize the object per se. The latter gives a relational description of the object considering how the visual properties are interdependent. The method considers SDALF as feature-based description: SDALF segregates salient body parts, exploiting symmetry and asymmetry principles. Afterwards, the parts are described by color, texture and region-based features. As relation-based description we consider the covariance of features, recently employed for re-identification: in practice, the parts found by SDALF are additionally encoded as covariance matrices, capturing structural properties otherwise missed. The resulting descriptor, dubbed SDALF+C, is superior to SDALF by about 2% and to the covariance-based description by a 53%, both in terms of average rank1 probability, considering 5 different multi-shot benchmark datasets (i-LIDS, ETHZ1,2,3 and CAVIAR4REID).
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