Image Analysis Based on Nonnegative/Binary Matrix Factorization
This work addresses image processing efficiency for computer vision applications, but it is incremental as it builds on existing matrix factorization methods.
The paper tackled image analysis by applying nonnegative/binary matrix factorization (NBMF) to facial images, achieving successful reconstruction and classification with NBMF converging in fewer iterations than standard NMF while performing comparably in classification tasks.
Using nonnegative/binary matrix factorization (NBMF), a matrix can be decomposed into a nonnegative matrix and a binary matrix. Our analysis of facial images, based on NBMF and using the Fujitsu Digital Annealer, leads to successful image reconstruction and image classification. The NBMF algorithm converges in fewer iterations than those required for the convergence of nonnegative matrix factorization (NMF), although both techniques perform comparably in image classification.