Fisher Linear Discriminant Analysis for Text-Image Combination in Multimedia Information Retrieval

Christophe Moulin (1) , Christine Largeron (1) , Christophe Ducottet (1) , Mathias Géry (1) , Cécile Barat (1)

1 LHC - Laboratoire Hubert Curien

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

Domains: Computer Science [cs]; Information Retrieval [cs.IR]; Computer Science [cs]; Computer Vision and Pattern Recognition [cs.CV]; Computer Science [cs]; Multimedia [cs.MM]

Christophe Moulin, Christine Largeron, Christophe Ducottet, Mathias Géry, Cécile Barat. Fisher Linear Discriminant Analysis for Text-Image Combination in Multimedia Information Retrieval. Pattern Recognition, 2014, 47 (1), pp.260-269. ⟨10.1016/j.patcog.2013.06.003⟩. ⟨ujm-00866140⟩