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For mixed occlusions, the error used to code the test image, called the discriminative error, and the error used to estimate the occlusion support, called the structural error, might have totally different behaviors. By combining the two various errors with the occlusion support, we present an extended error coding model, dubbed Mixed Error Coding (MEC). To further enhance discriminability and feature selection ability, we also incorporate into MEC the hidden feature selection technology of the subspace learning methods in the domain of the image gradient orientations. Experiments demonstrate the effectiveness and robustness of the proposed MEC model in dealing with mixed occlusions.","doi":null,"url":"https://ijcai.org/proceedings/2015/514","pdf":"https://ijcai.org/proceedings/2015/0514.pdf","markdown":"https://ijcai.org/proceedings/2015/514.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/514","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Special Track on Machine Learning","track":"Special Track on Machine Learning","number":514,"pages":{"first":3657,"last":3662},"keywords":[],"videos":[],"references":[{"position":1,"raw":"[Belhumeur et al., 1997] P Belhumeur, J Hespanha, and D Kriegman. 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