=Paper=
{{Paper
|id=Vol-1172/CLEF2006wn-ImageCLEF-WilhelmEt2006
|storemode=property
|title=ImageCLEF 2006 Experiments at the Chemnitz Technical University
|pdfUrl=https://ceur-ws.org/Vol-1172/CLEF2006wn-ImageCLEF-WilhelmEt2006.pdf
|volume=Vol-1172
|dblpUrl=https://dblp.org/rec/conf/clef/WilhelmE06a
}}
==ImageCLEF 2006 Experiments at the Chemnitz Technical University==
https://ceur-ws.org/Vol-1172/CLEF2006wn-ImageCLEF-WilhelmEt2006.pdf
ImageCLEF 2006 Exper iments at the Chemnitz Technical
Univer sity
Thomas Wilhelm, Maximilian Eibl
Chemnitz University of Technology
Faculty of Computer Science, Media Informatics
Strasse der Nationen 62, 09107 Chemnitz, Germany
thomas.wilhelm@s2000.tuchemnitz.de, eibl@informatik.tuchemnitz.de
Abstr act. We present a report about our participation in the ImageCLEF photo task 2006 and a
short description of our new framework for future use in further CLEF participations. We
described and analysed our participation in the monolingual English task. Special Lucenealigned
query expansion and histogram comparisons are helping to improve the baseline results.
ACM Categor ies and Subject Descr iptor s
H.3.1 Content Analysis and Indexing, H.3.3 Information Search and Retrieval, H.3.4 Systems and Software
Keywor ds
image retrieval, crosslanguage information retrieval, query expansion
1 Introduction
Our goal was to develop a new information retrieval framework which can handle different custom retrieval
mechanisms (i.e. Lucene, the GNU ImageFinding Tool and others). The framework should be so general that
one can use either one of the custom retrieval mechanisms or combine different ones. Therefore our efforts were
not focused on better results but to get the framework up and running.
2 System description
For our participation at CLEF 2006 we constructed a GUI (graphical user interface) quite easy to handle. It bases
on Lucene version 1.4 and includes several userconfigurable components. For example we wrote an Analyzer in
which TokenFilter s can be added and rearranged.
For the participation in ImageCLEF 2006 we needed to extend this system with image retrieval capabilities. But
instead of extending the old system we rewrote it and developed a general IR framework. This framework does
not necessarily require Lucene and makes it possible to build an abstract layer for other search engines like GIFT
(GNU ImageFinding Tool) and others.
The main component of our framework is the class Run, which includes all information required to perform and
repeat searches. A Run contains
· the Topics, which can be preprocessed by TopicFilters
· an Index, which was created by an Indexer from a DataCollection
· a search method called Searcher , which can read and search the Index
· and HitSets, which contain the results.
To use another search engine one will have to extend the abstract classes Indexer and Searcher . To use another
data collection type (e.g. GIRT4, IAPR, ...) one will have to extend the abstract class DataCollection.
In order to merge results there is a special extension of the class Searcher : the MergeSearcher. It combines
several Searcher s and uses an abstract class Merger to merge their results.
2.1 Lucene
The text search engine used is Lucene version 1.9 with an adapted Analyzer . As described above we create a
LuceneIndexer and an abstract LuceneSearcher that provides basic abilities to underlying Searcher s like a
selectable Lucene Analyzer . The Analyzer is based on the StandardTokenizer and LowerCaseFilter by Lucene
and is enhanced by a custom Snowball filter for stemming. We also added a positional stop word filter and used
the stop word list suggested on the CLEF website. The Analyzer details (stemmer and stop word list) can be
configured through a GUI. The LuceneStandardSearcher works with the Lucene MultiFieldQueryParser and
searches in specified fields of the index.
2.2 GIFT (GNU ImageFinding Tool)
The GIFT was not used for a submitted run but it should be mentioned here to show the abilities of the
framework. We only implemented a Searcher, which can access a GIFT server and takes images to generate
queries by example. The underlying index must be created with the command line utility provided by the GIFT.
2.3 IAPR Data Collection
To access the IAPR data collection of ImageCLEF we implemented the class IaprDataCollection that provides
two special options for the DataDocuments to contain special data:
· option 1: the DataDocuments contain the annotations
· option 2: the DataDocuments contain the histograms
In both cases the associated images can be retrieved with the help of the DataDocument.
3 Description of the runs submitted
We focused on the monolingual task using the English topics and annotations.
The Lucene index of the annotations contains all fields. All fields are indexed and tokenized except DATE,
which is not tokenized. The DOCNO is the only field which is stored, resulting in a quite small index (only 2
MB).
We used the default BooleanQuery from Lucene with no further weighting of any field pairs in order to avoid
any corpus specific weighting.
The default Lucene search classes (default similarity, etc.) were used to search the topicannotation field pairs as
shown in table 1.
Topic field Annotations searched
TITLE TITLE, LOCATION, DESCRIPTION, NOTES
NARR DESCRIPTION, NOTES
Table 1. Topicannotation field pairs
Our mechanism of query expansion is adapted to the Luceneindex and is, though simple, a little bit slow. First
of all a list of assumable relevant documents is identified. Then the Lucenegenerated index is checked for terms
also contained in documents in this list. These terms, their frequency and the number of documents in which they
are found are stored in a second list. Frequency is used to apply a threshold: only terms with a high frequency are
used to expand the query. The new terms are weighted by the ratio of its frequency and the number of containing
documents.
For all runs, except “tucEEANT”, the query expansion was applied once, using the first 20 results of the original
query.
In the run “tucEEAFT2” the query expansion is applied twice. The first time using the annotations of the
example images and the second time using the first 20 results of the original query.
To speed up the colour histogram comparison, we also created an index for the histogram data. For each image
the histograms for the three components hue, saturation and brightness are stored and can be retrieved by the
DOCNO. Though we use a Lucene index, it is used more like a database.
For the run “tucEEAFTI” all results of the textual search are scored using the Euclidean distance of their colour
histograms. The histogram distance makes 30% of the new score. The remaining 70% are taken from the original
score.
4 Results
Table 2 shows our results at ImageCLEF 2006. To compare our results, the top four runs of the monolingual
English task are included in table 2.
Rank Rank
System MOD A/M FB QE MAP
ENEN ALL
1 1 Cindi_Exp_RF MIXED MANUAL WITH YES 0,3850
2 2 Cindi_TXT_EXP TEXT MANUAL WITH YES 0,3749
3 3 IPALPWPFB3 MIXED AUTO WITHOUT 0,3337
4 5 NTUENENAUTOFBTXTIMG MIXED AUTO WITH 0,2950
5 16 tucEEAFT2 TEXT AUTO WITH YES 0,2436
17 33 tucEEAFTI MIXED AUTO WITH YES 0,1856
18 34 tucEEAFT TEXT AUTO WITH YES 0,1856
23 47 tucEEANT TEXT AUTO WITHOUT 0,1714
Table 2. Ranks of our submitted runs and the first four positions
As one can see, run “tucEEAFT2” is our best run which is no surprise if one takes into account that three
definitely relevant documents are used for query expansion. Our second best run is “tucEEAFTI” which shows
that the additional histogram comparison produces better results than without it.
Fig. 1. Recallprecision graphs for our submitted runs
5 Conclusion
The good results in ImageCLEF 2006 show that our framework works and that our first implemented search
methods provide good results.
The normal query expansion improves the mean average precision by more than 1%. Considering the poor
overall mean average precision this seems to be a good improvement of the results. Although the histogram
comparison is a very simple and presumably poor measurement to compare images, it improves our textonly
retrieval slightly.
The next steps to improve the results is to implement a better image retrieval algorithm due to the weak
performance of the histogram comparison.
Refer ences
1. Stevens, J. S., Husted T., Cutting D., & Carlson P. (2005). Apache Lucene Overview Apache Lucene.
Retrieved August 10, 2006, from http://lucene.apache.org/java/.
2. Grubinger M., Leung C., & Clough P. (2005). The IAPR Benchmark for Assessing Image Retrieval
Performance in Cross Language Evaluation Tasks. Retrieved August 10, 2006, from
http://ir.shef.ac.uk/cloughie/papers/muscleimageclef2005.pdf.
3. Roos, M. (2005). The GNU ImageFinding Tool GNU Project Free Software Foundation (FSF).
Retrieved August 10, 2006, from http://www.gnu.org/software/gift/.