SAC-NMF-Driven Graphical Feature Analysis and Applications
| Autor: | Haohao Li, Zhiyang Li, Shengfa Wang, Nannan Li |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
sparseness
lcsh:Computer engineering. Computer hardware business.industry Computer science Feature vector graphical application 020207 software engineering Pattern recognition lcsh:TK7885-7895 02 engineering and technology Matrix decomposition Non-negative matrix factorization Computer graphics non-negative matrix factorization descriptors Factorization Feature (computer vision) Simple (abstract algebra) Hidden variable theory 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing bases Artificial intelligence business |
| Zdroj: | Machine Learning and Knowledge Extraction, Vol 2, Iss 34, Pp 630-646 (2020) Machine Learning and Knowledge Extraction Volume 2 Issue 4 Pages 34-646 |
| ISSN: | 2504-4990 |
| Popis: | Feature analysis is a fundamental research area in computer graphics meanwhile, meaningful and part-aware feature bases are always demanding. This paper proposes a framework for conducting feature analysis on a three-dimensional (3D) model by introducing modified Non-negative Matrix Factorization (NMF) model into the graphical feature space and push forward further applications. By analyzing and utilizing the intrinsic ideas behind NMF, we propose conducting the factorization on feature matrices constructed based on descriptors or graphs, which provides a simple but effective way to raise compressed and scale-aware descriptors. In order to enable part-aware model analysis, we modify the NMF model to be sparse and constrained regarding to both bases and encodings, which gives rise to Sparse and Constrained Non-negative Matrix Factorization (SAC-NMF). Subsequently, by adapting the analytical components (including hidden variables, bases, and encodings) to design descriptors, several applications have been easily but effectively realized. The extensive experimental results demonstrate that the proposed framework has many attractive advantages, such as being efficient, extendable, and so forth. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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