Image annotation using multi-view non-negative matrix factorization with different number of basis vectors

Abstract

Automatic Image Annotation (AIA) helps image retrieval systems by predicting tags for images. In this paper, we propose an AIA system using Non-negative Matrix Factorization (NMF) framework. The NMF framework discovers a latent space, by factorizing data into a set of non-negative basis and coefficients. To model the images, multiple features are extracted, each one represents images from a specific view. We use multi-view graph regularization NMF and allow NMF to choose a different number of basis vectors for each view. For tag prediction, each test image is mapped onto the multiple latent spaces. The distances of images in these spaces are used to form a unified distance matrix. The weights of distances are learned automatically. Then a search-based method is used to predict tags based on tags of nearest neighbors’. We evaluate our method on three datasets and show that it is competitive with the current state-of-the-art methods.