|Author||Moulin, Christophe ♦ Ducottet, Christophe. ♦ Largeron, Christine|
|Subject Domain (in DDC)||Computer science, information & general works ♦ Data processing & computer science|
|Subject Keyword||Visual Term ♦ Performance Improvement ♦ Multimedia Information Retrieval Experiment ♦ Visual Information ♦ Overall Aim ♦ Local Feature ♦ Several Modality ♦ Text Query ♦ Textual Visual Term ♦ Textual Term Correspond ♦ Image Modality ♦ Pure Textual Model ♦ Multimedia Query ♦ Worth Result ♦ Similarity Score ♦ Visual One ♦ Visual Word ♦ Information Need ♦ Ranked List ♦ Multimedia Document Model ♦ Several Aspect ♦ Imageclef Track ♦ Relevant Document ♦ Image Vocabulary ♦ Idf Approach ♦ Initial Result ♦ Image Region ♦ Document Vector ♦ Okapi Method ♦ Multimedia Model ♦ Colour Property|
|Description||This paper reports our multimedia information retrieval experiments carried out for the ImageCLEF track (ImageCLEFwiki). The task is to answer to user information needs, i.e. queries which may be composed of several modalities (text, image, concept) with ranked lists of relevant documents. The purpose of our experiments is twofold: firstly, our overall aim is to develop a multimedia document model combining text and/or image modalities. Secondly, we aim to compare results of our model using a multimedia query with a text only model. Our multimedia document model is based on a vector of textual and visual terms. The textual terms correspond to words. The visual ones result from local colour de-scriptors which are automatically extracted and quantized by k-means, leading to an image vocabulary. They represent the colour property of an image region. To perform a query, we compute a similarity score between each document vector (textual + visual terms) and the query using the Okapi method based on the tf.idf approach. We have submitted 6 runs either automatic or manual, using textual, visual or both information. Thanks to these 6 runs, we aim to study several aspects of our model, as the choice of the visual words and local features, the way of combining textual and visual words for a query and the performance improvements obtained when adding visual information to a pure textual model. Concerning the choice of the visual words, results show us that they are significant in some cases where the visualness of the query is meaningful. The conclusion about the combination of textual and visual words is surprising. We obtain worth results when we add directly the text to the visual words. Finally, results also inform that visual information bring complementary relevant documents that were not found with the text query. These initial results are promising and encourage the development of our multimedia model. 1|
|Educational Role||Student ♦ Teacher|
|Age Range||above 22 year|
|Education Level||UG and PG ♦ Career/Technical Study|
|Learning Resource Type||Article|
|Publisher Institution||In ImageCLEF 2008|
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