Different Multimodal Approaches using IR-n in ImageCLEFphoto 2008.
Sergio Muñoz, Fernando Llopis, Rafael Muñoz · CLEF (Working Notes) · 2008
This paper describes the approach of the university of Alicante to the problem of finding a suitable handling of multimodal sources within the ImageCLEF ad-hoc competition. We have worked on to add modifications to the most common multimodal techniques used in the image retrieval area in order to improve their performance. Moreover, we have added a clustering module in order to increase the number of different clusters that can be found within the top 20 images returned. Finally, we have studied the effect of using visual concepts in the retrieval phase and in the clustering phase. We can see in the results that with these multimodal techniques we have improved up to a 27% our results in a MAP way, respect the ones obtained using our last year configuration a textual run using PRF -. Furthermore, we have seen that the use of LCA in a multimodal way outperforms clearly the MAP and P20 results obtained with other common methods used it has obtained 0.3436 MAP, 4th place in the published task results, and 0.4564 P20, 5th place in the published task results -. Finally our TFIDF re-ranking run method has showed the best behaviour for the top 20 documents returned from our submissions, obtaining a F-measure value of 0.4051 based on P20 and CR20 measures -. It makes us to conclude that the combination of these two mutimodal techniques will be the key for improving the performance in our system in future works.