Multi-View Nonnegative Matrix Factorization for Clothing Image Characterization

Due to the ambiguity in describing and discriminating between clothing images of different styles, it has been a challenging task to solve clothing image characterization problems. Based on the use of multiple types of visual features, we propose a novel multi-view nonnegative matrix factorization (NMF) algorithm for solving the above task. Our multi-view NMF not only observes image representations for describing clothing images in terms of visual appearances, an optimal combination of such features for each clothing image style would also be learned, while the separation between different image styles can be preserved. To verify the effectiveness of our method, we conduct experiments on two image datasets, and we confirm that our method produces satisfactory performance in terms of both clustering and categorization.