Fisher Linear Discriminant Analysis for text-image combination in multimedia information retrieval

• We model text and image documents with bag-of-words approach.
• We Fisher LDA for learning weights assigned to each modality.
• We experiment our model on ImageCLEF datasets 2008 and 2009.
• Our model outperforms the use of the single textual modality.
• Our method provides a nearly optimal learning with an efficient computation.

With multimedia information retrieval, combining different modalities – text, image, audio or video provides additional information and generally improves the overall system performance. For this purpose, the linear combination method is presented as simple, flexible and effective. However, it requires to choose the weight assigned to each modality. This issue is still an open problem and is addressed in this paper.Our approach, based on Fisher Linear Discriminant Analysis, aims to learn these weights for multimedia documents composed of text and images. Text and images are both represented with the classical bag-of-words model. Our method was tested over the ImageCLEF datasets 2008 and 2009. Results demonstrate that our combination approach not only outperforms the use of the single textual modality but provides a nearly optimal learning of the weights with an efficient computation. Moreover, it is pointed out that the method allows to combine more than two modalities without increasing the complexity and thus the computing time.