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Attribute prediction with long-range interactions via path coding
- Source :
- ICIP
- Publication Year :
- 2014
- Publisher :
- IEEE, 2014.
-
Abstract
- Due to the describable or human-nameable nature of visual attributes, the appropriate utilization of attributes has been receiving much attention in recent years in many applications. Motivated by the assumption that the long-range interactions between attributes can boost image understanding and classification, path coding is utilized in this paper to model the long-range interactions between attributes for the attribute prediction, we call it attribute prediction via a path coding penalty (abbreviated as AP2CP). AP2CP not only introduces structured sparsity penalties over paths on a directed acyclic graph, but also captures the intrinsical long-range dependent interactions between attributes. The proposed AP2CP can be efficiently solved by leveraging network flow optimization. The experiments show that the proposed AP2CP achieves a better performance in attribute prediction.
Details
- Database :
- OpenAIRE
- Journal :
- 2014 IEEE International Conference on Image Processing (ICIP)
- Accession number :
- edsair.doi...........10704c550cf3d269ac57a8a6a3ca5963