Experiments for the ImageCLEF 2007 Photographic Retrieval Task

Abstract

This article describes the con guration of the experiments that we submitted for the ImageCLEF Photographic Retrieval Task. We used a redesigned version of our last years retrieval system prototype (see [1] for details). The translation of the topics for our cross-lingual experiments was realized with a plug-in to access the Google Translate [2] service. We used thesauri from OpenO ce [3] to expand the queries for better retrieval performance. This year, we submitted 11 runs, whereof only one was completely automatic. In all our experiments mixed modality was applied, i.e. we used text retrieval and content-based image retrieval for re-ranking. The evaluation results show that most of our experiments achieved very strong retrieval performance.

Introduction and outline

collection. We used thesauri from OpenO ce [ 3 ] by applying a threshold technique to obtain a number of terms for each query. The baseline of all our experiments was a classic text retrieval run. In a second step the results of the text retrieval were re-ranked based on image content descriptors. We applied the MPEG-7 descriptors EdgeHistogram and ScalableColor from the Caliph and Emir project [ 5 ] that were calculated from the example image of each topic. Finally, we used a manual feedback strategy to enhance retrieval performance in all our setups except the baseline run. The feedback strategy was to assess a certain number of the top documents and to apply a feedback algorithm that uses the annotations from the relevant documents. 3

Con guration of submitted runs

The detailed setup of our experiments are presented in the following subsections. 3.1

Monolingual We submitted 5 monolingual experiments in total, whereof one was the completely automatic baseline run ( rst row in table 1). We also submitted cross-language experiments for all target collections. The translation was realized with a plug-in that is capable to access the Google Translate [ 2 ] service. We also used the thesauri based query expansion approach that was mentioned before. Table 2 shows the setup of the individual cross-language runs.

identi er cut-EN2ES-F20 cut-ZHS2EN-F20 cut-DE2EN-F20 cut-IT2EN-F20 cut-FR2EN-F20 cut-FR2DE-F20

Results

The results of our submitted runs are summarized in table 3. It can be seen that our monolingual english experiment performed best. Furthermore, one can observe that monolingual retrieval performance for english and spanish annotations is very good, while monolingual retrieval on german annotations is quite bad in comparison. Another interesting observation is the result for the cross-lingual experiment with english topics on spanish annotations, which performs better than all cross-lingual runs on the english annotations. Our experiments showed that the manual feedback strategy is a promising approach for this year's ImageCLEF Photographic Retrieval Task. But also the combination of text retrieval and well-known content-based image descriptors as well as the application of thesauri based query expansion in this domain - with a small amount of textual metadata - was important for good retrieval performance.

References

[1] Wilhelm , T. & Eibl , M. ( 2006 ). ImageCLEF 2006 Experiments at the Chemnitz Technical University . In Working Notes for the CLEF 2006 Workshop , 20 - 22 September, Alicante, Spain. Retrieved August 17 , 2007 , from CLEF Web site: http://www.clef-campaign.org/2006/working_notes/workingnotes2006/wilhelmCLEF2006l.pdf

[2] Google ( 2007 ). Google Translate BETA . Retrieved August 17 , 2007 , from Google Web site: http://www.google.com/translate_t

[3] OpenO ce ( 2007 ). OpenO ce . Retrieved August 17 , 2007 , from OpenO ce Web site: http://www.openoffice.org/

[4] Grubinger , M. & Cloug , P. & Hanburry , A. & Muller, H. ( 2007 ). Overview of the ImageCLEFphoto 2007 Photographic Retrieval Task . In Working Notes for the CLEF 2007 Workshop , 19 - 21 September, Budapest, Hungary. To appear.

[5] Lux , M. ( 2004 - 2007 ). Calpih & Emir. Retrieved August 17 , 2007 , from SemanticMetadata Web site: http://www.semanticmetadata.net/