Name
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 deformable 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. |
Year | Venue | Field |
|---|---|---|
2015 | IJCAI | Matching pursuit,Neural coding,Inference,Computer science,Support vector machine,Algorithm,Object structure,Parsing,Computational complexity theory,Belief propagation |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
14 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ling Cai | 1 | 3 | 2.42 |
| Rongrong Ji | 2 | 3616 | 189.98 |
| Wei Liu | 3 | 4041 | 204.19 |
| Gang Hua | 4 | 2796 | 157.90 |