Impact of visual information on text and content based image retrieval
Nowadays, multimedia documents composed of text and images are increasingly used, thanks to the Internet and the increasing capacity of data storage. It is more and more important to be able to retrieve needles in this huge haystack. In this paper, we present a multimedia document model which combines textual and visual information. Using a bag-of-words approach, it represents a textual and visual document using a vector for each modality. Given a multimedia query, our model combines scores obtained for each modality and returns a list of relevant retrieved documents. This paper aims at studying the influence of the weight given to the visual information relatively to the textual information. Experiments on the multimedia ImageCLEF collection show that results can be improved by learning this weight parameter.