دورية أكاديمية
Robust Kernelized Bayesian Matrix Factorization for Video Background/Foreground Separation
| العنوان: | Robust Kernelized Bayesian Matrix Factorization for Video Background/Foreground Separation |
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| المؤلفون: | Xie, HB, Li, C, Xu, RYD, Mengersen, K |
| سنة النشر: | 2019 |
| المجموعة: | University of Technology Sydney: OPUS - Open Publications of UTS Scholars |
| مصطلحات موضوعية: | Artificial Intelligence & Image Processing |
| الوصف: | © Springer Nature Switzerland AG 2019. Development of effective and efficient techniques for video analysis is an important research area in machine learning and computer vision. Matrix factorization (MF) is a powerful tool to perform such tasks. In this contribution, we present a hierarchical robust kernelized Bayesian matrix factorization (RKBMF) model to decompose a data set into low rank and sparse components. The RKBMF model automatically infers the parameters and latent variables including the reduced rank using variational Bayesian inference. Moreover, the model integrates the side information of similarity between frames to improve information extraction from the video. We employ RKBMF to extract background and foreground information from a traffic video. Experimental results demonstrate that RKBMF outperforms state-of-the-art approaches for background/foreground separation, particularly where the video is contaminated. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | unknown |
| ردمك: | 978-3-030-37598-0 3-030-37598-6 |
| العلاقة: | 2019, 11943 LNCS pp. 484 - 495; http://hdl.handle.net/10453/138759Test |
| الإتاحة: | http://hdl.handle.net/10453/138759Test |
| رقم الانضمام: | edsbas.3291BF1 |
| قاعدة البيانات: | BASE |
| ردمك: | 9783030375980 3030375986 |
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