公开(公告)号：US20250036717A1

公开(公告)日：2025-01-30

申请号：US18911883

申请日：2024-10-10

Applicant: Chengdu University of Technology

Inventor： Zhongli ZHOU ,  Ran ZHOU ,  Changjie CAO ,  Bingli LIU ,  Yunhui KONG ,  Cheng LI ,  Yueyun LIU

IPC: G06F17/16 ,  G06N3/09 ,  G06V10/764 ,  G06V10/77 ,  G06V10/774 ,  G06V10/776

Abstract: A robust nonnegative matrix factorization (RNMF) method, in which an image sample set is split into a training set and a test set. The training set and the test set are normalized to map the image data from [0, 255] to [0, 1]. The training set matrix is pretrained by RNMF for decomposition. l2,1-deep incremental nonnegative matrix factorization (l2,1-DINMF) model is construed. The l2,1-DINMF model is configured to decompose the training set matrix into l+1 factors. After the basis matrix has been updated, and the samples of the training set and samples to be recognized are projected into a feature space. Feature representations of the test set are classified by a trained SVM classifier to obtain a predicted label, and the predicted label is compared with an actual label of the test set to calculate a recognition accuracy.