Online Matrix Factorization for Multimodal Image Retrieval

Abstract

AbstractIn this paper, we propose a method to build an index for image search using multimodal information, that is, using visual features and text data simultaneously. The method combines both data sources and generates one multimodal representation using latent factor analysis and matrix factorization. One remarkable characteristic of this multimodal representation is that it connects textual and visual content allowing to solve queries with only visual content by implicitly completing the missing textual content. Another important characteristic of the method is that the multimodal representation is learned online using an efficient stochastic gradient descent formulation. Experiments were conducted in a dataset of 5,000 images to evaluate the convergence speed and search performance. Experimental results show that the proposed algorithm requires only one pass through the data set to achieve high quality retrieval performance.KeywordsImage RetrievalMatrix FactorizationText DataRetrieval PerformanceNonnegative Matrix FactorizationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.