MIRACLE (FI) at ImageCLEFphoto 2009

The Miracle-FI participation at ImageCLEF 2009 photo retrieval task main goal was to improve the merge of content-based and text-based techniques in our experiments. The global system includes our own implemented tool IDRA (InDexing and Retrieving Automatically), and the Valencia University CBIR system. Analyzing both “topics_part1.txt” and “topics_part2.txt” task topics files, we have built different queries files, eliminating the negative sentences with the text from title and clusterTitle or clusterDescription, one query for each cluster (or not) of each topic from 1 to 25 and one for each of the three images of each topic from 26 to 50. In the CBIR system the number of low-level features has been increased from the 68 component used at ImageCLEF 2008 up to 114 components, and in this edition only the Mahalanobis distance has been used in our experiments. Three different merging algorithms were developed in order to fuse together different results lists from visual or textual modules, different textual indexations, or cluster level results into a unique topic level results list. For the five runs submitted we observe that MirFI1, MirFI2 and MifFI3 obtain quite higher precision values than the average ones. Experiment MirFI1, our best run for precision metrics (very similar to MirFI2 and MirFI3), appears in the 16th position in R-Precision classification and in the 19th in MAP one (from a total of 84 submitted experiments). MirFI4 and MirFI5 obtain our best diversity values, appearing in position 11th (over 84) in cluster recall classification, and being the 5th best group from all the 19 participating ones

Some Results Using Different Approaches to Merge Visual and Text-Based Features in CLEF’08 Photo Collection

This paper describes the participation of the MIRACLE team at the ImageCLEF Photographic Retrieval task of CLEF 2008. We succeeded in submitting 41 runs. Obtained results from text-based retrieval are better than content-based as previous experiments in the MIRACLE team campaigns [5, 6] using different software. Our main aim was to experiment with several merging approaches to fuse text-based retrieval and content-based retrieval results, and it happened that we improve the text-based baseline when applying one of the three merging algorithms, although visual results are lower than textual ones

Multimedia Retrieval by Means of Merge of Results from Textual and Content Based Retrieval Subsystems

The main goal of this paper it is to present our experiments in ImageCLEF 2009 Campaign (photo retrieval task). In 2008 we proved empirically that the Text-based Image Retrieval (TBIR) methods defeats the Content-based Image Retrieval CBIR “quality” of results, so this time we developed several experiments in which the CBIR helps the TBIR. The TBIR System [6] main improvement is the named-entity sub-module. In case of the CBIR system [3] the number of low-level features has been increased from the 68 component used at ImageCLEF 2008 up to 114 components, and only the Mahalanobis distance has been used. We propose an ad-hoc management of the topics delivered, and the generation of XML structures for 0.5 million captions of the photographs (corpus) delivered. Two different merging algorithms were developed and the third one tries to improve our previous cluster level results promoting the diversity. Our best run for precision metrics appeared in position 16th, in the 19th for MAP score, and for diversity value in position 11th, for a total of 84 submitted experiments. Our best and “only textual” experiment was the 6th one over 41

La herramienta IDRA (Indexing and Retrieving Automatically)

Se presenta brevemente la herramienta IDRA, con licencia GPL 3.0, que a partir de unas funcionalidades básicas, facilita la agregación de nuevas funcionalidades para la investigación en recuperación de información

Research in Linguistic Engineering: Resources and Tools

In this paper we are revisiting some of the resources and tools developed by the members of the Intelligent Systems Research Group (GSI) at UPM as well as from the Information Retrieval and Natural Language Processing Research Group (IR&NLP) at UNED. Details about developed resources (corpus, software) and current interests and projects are given for the two groups. It is also included a brief summary and links into open source resources and tools developed by other groups of the MAVIR consortium