Large scale image indexing using online non-negative semantic embedding

Abstract

This paper presents a novel method to address the problem of indexing a large set of images taking advantage of associated multimodal content such as text or tags. The method finds relationships between the visual and text modalities enriching the image content representation to improve the performance of content-based image search. This method finds a mapping that connects visual and text information that allows to project new (annotated and unannotated) images to the space defined by semantic annotations, this new representation can be used to search into the collection using a query-by-example strategy and to annotate new unannotated images. The principal advantage of the proposed method is its formulation as an online learning algorithm, which can scale to deal with large image collections. The experimental evaluation shows that the proposed method, in comparison with several baseline methods, is faster and consumes less memory, keeping a competitive performance in content-based image search. © Springer-Verlag 2013.