Abstract
This paper proposes a novel method for image indexing based on an online learning approach which can deal with large repositories of images. The proposed method is based on a semantic embedding strategy which models a mapping between visual and text representations. This method enhances the image representation by taking advantage of the text annotations associated to the images, which have a rich and clean semantic interpretation. Once the mapping is learned, a new (annotated or unannotated) image can be projected to the space defined by semantic annotations. In this manner, this method can be used to search into the collection using an image as query (query-by-example strategy) and to annotate new unannotated images. The main drawback of semantic embedding strategies is that the associated algorithms are computationally expensive, making them infeasible for large data collections. In order to address this issue, the proposed method is formulated as an online learning algorithm using the stochastic gradient descent approach, which can scale to deal with large image collections. According with the experimental evaluation, the proposed method, in comparison with several baseline methods, is faster and consumes less memory, without degradation in the performance in content-based image search.