=Paper=
{{Paper
|id=Vol-1174/CLEF2008wn-ImageCLEF-WilhelmEt2008b
|storemode=property
|title=The Xtrieval Framework at CLEF 2008: ImageCLEF Wikipedia MM task
|pdfUrl=https://ceur-ws.org/Vol-1174/CLEF2008wn-ImageCLEF-WilhelmEt2008b.pdf
|volume=Vol-1174
|dblpUrl=https://dblp.org/rec/conf/clef/WilhelmKE08a
}}
==The Xtrieval Framework at CLEF 2008: ImageCLEF Wikipedia MM task==
https://ceur-ws.org/Vol-1174/CLEF2008wn-ImageCLEF-WilhelmEt2008b.pdf
The Xtrieval Framework at CLEF 2008:
ImageCLEF Wikipedia MM task
Thomas Wilhelm, Jens Kürsten, and Maximilian Eibl
Chemnitz University of Technology
Faculty of Computer Science, Dept. Computer Science and Media
09107 Chemnitz, Germany
[ thomas.wilhelm | jens.kuersten | maximilian.eibl ] at cs.tu-chemnitz.de
Abstract. This paper describes our participation at the ImageCLEF Wikipedia MM task. We used
our Xtrieval framework for the preparation and execution of the experiments. We submitted 4
experiments in total. The results of these experiments were mixed. The text-only experiment
scored second best with a mean average precision (MAP) of 0.2166. In combination with image
based features the MAP dropped to 0.2138. With the addition of our thesaurus based query
expansion it scored best with a MAP of 0.2195. Without query expansion and with the inclusion of
the provided concepts the lowest MAP of 0.2048 was achieved, but there were 23 more relevant
documents retrieved than in all 3 other experiments. Furthermore, the retrieval speed and
comparison operations for vectors could be speeded up by implementing an interface to the
PostgreSQL database.
Categories and Subject Descriptors
H.3 [Information Storage and Retrieval]: H.3.1 Content Analysis and Indexing; H.3.3 Information Search and
Retrieval
Keywords
Evaluation, Cross-Language Information Retrieval, Content-based Image Retrieval, Query Expansion,
Experimentation
1 Introduction
For the ImageCLEF 2008: Wikipedia MM task a large set of data is supplied. Additional to the images and texts
a 120 dimensional feature vector and concepts were available. This encouraged us to find a solution for vector
storage, retrieval and comparison. Therefore we implemented an interface to index and search data into a
PostgreSQL database. Additional to indexing the supplied feature vector we also indexed the MPEG-7
descriptors computed by Caliph & Emir.
2 Experiment Setup
We used our base system from the last year (see [1], [2] and [3]) with the following setup: Apache Lucene,
customized analyzer with positional stopword1 removal and Snowball stemmer2. For the content-based image
retrieval we used Caliph & Emir as described below.
For query expansion a thesaurus was used. The parameters of the last year were further tuned to reduce unfitting
synonyms. As source for the thesauri we still use OpenOffice.org3.
1
http://members.unine.ch/jacques.savoy/clef/index.html
2
http://snowball.tartarus.org/
3
http://wiki.services.openoffice.org/wiki/Dictionaries
The MPEG-7 features were calculated by Caliph & Emir (see [4]). Contrary to our experiments of the last year
the MPEG-7 descriptors were not stored as text representations in Lucene 4, but as vectors in a PostgreSQL5
database. PostgreSQL was chosen because it supports arrays as data types. In fact it is not necessary to know the
actual size of the arrays at design time. This approach is expected to achieve a much higher retrieval speed and it
is possible to use descriptors of Caliph & Emir which has no string representation implemented (e.g. the
dominant color descriptor). The supplied 120 dimensional vectors are stored in the PostgreSQL database as well.
The computations of the distance measures were externalized into the PostgreSQL database by implementing the
algorithms as stored procedures in PL/pgSQL 6. PL/pgSQL is an internal programming language of PostgreSQL
which adds support for additional logic to SQL such as control structures. The following algorithms are
implemented so far: cosine similarity, Dice coefficient, Euclidean metric, intersection, Jaccard similarity
coefficient. The main advantage is the reduction of extra round trips between our application and the database
server. On the other hand the speed could be reduced by the fact that PL/pgSQL is an interpreted language.
All topics were preprocessed ad-hoc to retrieve all needed resources to perform the experiments. Especially the
example images were retrieved and analyzed in advance.
3 Results
The results in table 1 show that the text-only results can only be marginally improved by additional data. Just
experiment “cut-mix-concepts” retrieved more relevant documents in total, but with a lower MAP than all other
experiments.
Table 1. Retrieval results
relevant
feedback/ retrieved
run type modality MAP retrieved
expansion documents
documents
cut-txt-a auto txt nofb 0.2166 52623 3111
cut-mix auto txtimg nofb 0.2138 52623 3111
cut-mix-qe auto txtimg qe 0.2195 52623 3111
cut-mix-concepts auto txtimgcon nofb 0.2048 70803 3134
Our baseline is “cut-txt-a”, which retrieved a total of 3111 relevant documents and reached a mean average
precision (MAP) of 0.2166. By adding the content-based image features, which consist of the 120 dimensional
feature vector and four MPEG-7 descriptors (scalable color, edge histogram, color layout and dominant color
descriptor), the MAP decreased to 0.2138. This is a hint towards the low visual similarity between relevant
pictures, which attract our attention during the relevance assessment process. After the preprocessing of the
topics with our query expansion our highest MAP of 0.2195 was achieved. The inclusion of the concepts scored
the worst MAP of 0.2048, but retrieved 23 more relevant documents than any other of our experiments.
4 Future Work
The PostgreSQL database support many other programming languages to implement stored procedures, i.e.
PL/Tcl, PL/Perl, PL/Python and PL/Java 7. Because the retrieval system itself is written in Java it would be
suitable to use PL/Java as programming language.
4
http://lucene.apache.org
5
http://www.postgresql.org
6
http://www.postgresql.org/docs/current/static/plpgsql.html
7
http://pgfoundry.org/projects/pljava/
We also intend to implement our own content-based image retrieval algorithms on the basis of vectors stored in a
PostgreSQL database.
References
[1] T. Wilhelm, J. Kürsten, and M. Eibl, “Experiments for the ImageCLEF 2007 Photographic Retrieval Task”;
http://clef-campaign.org/2007/working_notes/wilhelmCLEF2007.pdf.
[2] J. Kürsten, T. Wilhelm, and M. Eibl, “The xtrieval framework at clef 2007: Domain-specific track,” LNCS -
Advances in Multilingual and Multimodal Information Retrieval, C. Peters et al., ed., Berlin: Springer
Verlag, 2008.
[3] T. Wilhelm, J. Kürsten, and M. Eibl, “Extensible retrieval and evaluation framework: Xtrieval,” LWA 2008:
Lernen - Wissen - Adaption, Würzburg: 2008.
[4] M. Lux, W. Klieber, and M. Granitzer, “Caliph & Emir: Semantics in Multimedia Retrieval and
Annotation,” 19th International CODATA Conference, 2004.