Person Recognition in Video Based on Unsupervised Method

doi:10.3969/j.issn.1006-2475.2014.12.011

Abstract

Abstract: In this paper, we propose a novel method for person recognition based on a salient learning with unsupervised. The salient features can be extracted without the person labels in the training procedure. First, we utilize patch matching with adjacency constrained to create dense correspondence between image pairs and it shows validity in processing misalignment problem caused by larger viewpoint and pose changes. Second, we learn person salience by applying an unsupervised approach. In order to improve the performance of experiment, the person salience is combined with patch matching. This approach on the VIPeR dataset is proved the effect is validated. And the performance is better than the eBiCov method and the eLDFV method.

Key words: image processing, unsupervised learning, person recognition, signification

CLC Number:

TP391

NING Bo, SONG Yan. Person Recognition in Video Based on Unsupervised Method[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2014.12.011.

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