Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
A vector space model for automatic indexing
Communications of the ACM
The Journal of Machine Learning Research
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Exploiting Visual Concepts to Improve Text-Based Image Retrieval
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Overview of the wikipediaMM task at ImageCLEF 2009
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
IEEE Transactions on Image Processing
Content-Based re-ranking of text-based image search results
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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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 relative to the textual information. Experiments on the multimedia ImageCLEF collection show that results can be improved by learning this weight parameter.