Details

Autor(en) / Beteiligte

• Yang, Zhangjing
• Zhang, Hui
• Xu, Danhua
• Zhang, Fanlong
• Yang, Guowei

Titel

Double truncated nuclear norm-based matrix decomposition with application to background modeling

Ist Teil von

• Journal of ambient intelligence and humanized computing, 2023-11, Vol.14 (11), p.14921-14930

Ort / Verlag

Berlin/Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2023

Link zum Volltext

Quelle

SpringerLink (Online service)

Beschreibungen/Notizen

• Many topics in pattern recognition and machine learning, such as subspace learning, image restoration, background modeling, can be viewed as the matrix decomposing problem. Double nuclear norm-based matrix decomposition (DNMD) is a new emerging method for dealing with the image data corrupted by continuous occlusion. The method uses a unified low rank assumption to characterize the real image data and continuous occlusion. However, one major limitation of the nuclear norm is that each singular value is treated equally, since the nuclear norm is defined as the sum of all singular values. Thus the rank function may not be well approximated in practice. To overcome this drawback, this paper presents double truncated nuclear norm-based matrix decomposition (DTNMD). The truncated nuclear norm can reflect the rank function more accurate and robust. Experimental results show encouraging results of the proposed algorithm in comparison to the state-of-the-art matrix completion methods on both synthetic and real visual datasets.