Multilinear sparse decomposition for best spectral bands selection
المؤلف | Bouchech, Hamdi Jamel |
المؤلف | Foufou, Sebti |
المؤلف | Abidi, Mongi |
تاريخ الإتاحة | 2016-05-16T10:57:31Z |
تاريخ النشر | 2014 |
اسم المنشور | Image and Signal Processing: 6th International Conference, ICISP 2014, Cherbourg, France, June 30-July 2, 2014. Proceedings |
المصدر | Scopus |
الاقتباس | Bouchech, Hamdi Jamel; Foufou, Sebti; Abidi, Mongi, "Multilinear sparse decomposition for best spectral bands selection, in Image and Signal Processing, vol. 8509 of Lecture Notes in Computer Science, pp. 384-391, Springer, 2014. |
الترقيم الدولي الموحد للكتاب | 978-3-319-07997-4 |
الترقيم الدولي الموحد للكتاب | 978-3-319-07998-1 |
الملخص | Optimal spectral bands selection is a primordial step in multispectral images based systems for face recognition. In this context, we select the best spectral bands using a multilinear sparse decomposition based approach. Multispectral images of 35 subjects presenting 25 different lengths from 480nm to 720nm and three lighting conditions: fluorescent, Halogen and Sun light are groupped in a 3-mode face tensor T of size 35x25x2 . T is then decomposed using 3-mode SVD where three mode matrices for subjects, spectral bands and illuminations are sparsely determined. The 25x25 spectral bands mode matrix defines a sparse vector for each spectral band. Spectral bands having the sparse vectors with the lowest variation with illumination are selected as the best spectral bands. Experiments on two state-of-the-art algorithms, MBLBP and HGPP, showed the effectiveness of our approach for best spectral bands selection. |
اللغة | en |
الناشر | Springer Verlag |
السلسلة | Lecture Notes in Computer Science |
الموضوع | HGPP MBLBP Multilinear sparse Spectral bands Tensor |
النوع | Conference Paper |
الصفحات | 384-391 |
رقم المجلد | 8509 |
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