Multimodal Image Annotation Using Non-negative Matrix Factorization

Visual content has become an important component of the web. In many cases, visual content is mixed with other modalities (e.g. text) that can be exploited to extract information and knowledge. This paper presents a strategy for mining multimodal visual content. The strategy encompasses two main components: a rich representation of the multimodal objects and a model for automatically annotating unannotated images. The proposed method has two distinguishing characteristics: it uses a bag-of-features representation for images and a non-negative matrix factorization algorithm to build a latent representation.