{"type":"ScholarlyArticle","title":"Modeling Inter- and Intra-Part Deformations for Object Structure Parsing","authors":[{"name":"Ling Cai","slug":"ling-cai","given":"Ling","family":"Cai","affiliation":""},{"name":"Rongrong Ji","slug":"rongrong-ji","given":"Rongrong","family":"Ji","affiliation":""},{"name":"Wei Liu","slug":"wei-liu","given":"Wei","family":"Liu","affiliation":""},{"name":"Gang Hua","slug":"gang-hua","given":"Gang","family":"Hua","affiliation":""}],"abstract":"Part deformation has been a longstanding challenge for object parsing, of which the primary difficulty lies in modeling the highly diverse object structures. To this end, we propose a novel structure parsing model to capture deformable object structures. The proposed model consists of two de-formable layers: the top layer is an undirected graph that incorporates inter-part deformations to infer object structures; the base layer is consisted of various independent nodes to characterize local intra-part deformations. To learn this two-layer model, we design a layer-wise learning algorithm,which employs matching pursuit and belief propagation for a low computational complexity inference. Specifically, active basis sparse coding is leveraged to build the nodes at the base layer, while the edge weights are estimated by a structural support vector machine. Experimental results on two benchmark datasets (i.e., faces and horses) demonstrate that the proposed model yields superior parsing performance over state-of-the-art models.","doi":null,"url":"https://ijcai.org/proceedings/2015/304","pdf":"https://ijcai.org/proceedings/2015/0304.pdf","markdown":"https://ijcai.org/proceedings/2015/304.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/304","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":"Vision and Perception","track":"Main Track","number":304,"pages":{"first":2148,"last":2153},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}