Image multi-label annotation based on supervised nonnegative matrix factorization with new matching measurement

Nonnegative Matrix Factorization (NMF) has been attracting many scholars in the fields of pattern recognition and data mining to study it since its inception. To date, a large number of variant methods have been proposed and applied in image retrieval and image Single-Label Annotation (SLA) successfully. However, the effectiveness of NMF for Multi-Label Annotation (MLA) encounters difficulties and is still an open topic. To meet this goal, this paper proposes a supervised NMF with new matching measurement to enhance MLA accuracy. In contrast with other NMF algorithms with sparse or discriminant constraints, the proposed NMF algorithm implements a supervised training method while integrates feature dimension reduction. What's more, we improve a novel matching measurement function by considering positive and negative samples respectively, which is proved to be more suitable for MLA. In addition, the proposed NMF object function is solved by using the projected gradient method, and image annotation can be achieved. Experiments results on NUSWIDE dataset showed that the proposed algorithm can achieve strong performance compared with existing algorithms in terms of False Rejection Rate (FRR) and False Acceptance Rate (FAR).