Cross-Language Evaluation
Forum
CLEF Workshop 2009
Poster Boaster Session
CLEF 2009
Poster Boaster Session
Features influencing retrieval in
Persian Language
Ljiljana Dolamic, Jacques Savoy
(Computer Science Departement
University of Neuchatel, Switzerland)
Track(s): Ad-hoc@CLEF
CLEF 2009
Poster Boaster Session
Evaluation
Features
• Indexing strategy
• Stopword list
removal
• Relevance
assessments
• Language
features
IR models
• Okapi
• DFR - PL2
• LM
.
• tf idf
CLEF 2009
Poster Boaster Session
Risk of not using Light Stemming:
Official
run
UniNEpe3
Query
Index
Model
Query Expansion
Single
MAP
TD
word
PL2
none
0.4057
TD
word
PL2
Roc 10 docs / 50 terms
0.4466
TD
TD
TD
word
plural
perstem
Okapi
Okapi
PB2
Roc 5 docs / 50 terms
Roc 5 docs / 70 terms
idf 10 docs / 50 terms
0.4228
0.4311
0.4462
TD
light
PL2
Roc 5 docs / 20 terms
0.4718
CLEF 2009
Poster Boaster Session
Comb
MAP
Z-Score
0.4663
Alicante at
CLEF 2009 Robust-WSD Task
Javi Fernández, Rubén Izquierdo and
José M. Gómez
(Department of Software and Computing
Systems and El Taller Digital, from University
of Alicante)
Track(s): Robust WSD (Ad Hoc)
CLEF 2009
CLEF
2009
Poster Boaster
Session
Poster Boaster Session
Semantic classes (I)
Goal: Explore the benefits of using semantic
classes in IR systems
WND (WordNet Domains)
BLC20 (Basic Level Concepts)
Results
GMAP: +1.77%
MAP: +0.64%
CLEF 2009
Poster Boaster Session
Query expansion re-ranking (II)
Goal: Explore the benefits of combining known
query expansion models with:
WordNet synsets’ relations
WordNet synsets’ distances
Results
GMAP: +9.97%
CLEF 2009
Poster Boaster Session
Thank you
Feel free to make any questions
See you at the poster session
CLEF 2009
Poster Boaster Session
CLEF topics and Wikipedia articles:
Did it blend?
Nuno Cardoso
Linguateca
Faculty of Sciences
University of Lisbon
Track: GikiCLEF
CLEF 2009
Poster Boaster Session
Motivation
Using Wikipedia to answer GikiCLEF topics proved to be
quite difficult.
Is it the task or the systems?
Wikipedia
raw knowledge
Did the systems mined Wikipedia
conveniently, or they did not
manage to scratch the surface?
Are we aware of the difficulties in
finding the right answers in Wikipedia?
Knowledge mined by a
GikiCLEF system
Let’s analyse the Wikipedia collection and the GikiCLEF task.
CLEF 2009
Poster Boaster Session
Questions addressed in the poster
In order to mine Wikipedia properly, we must have a better
insight on what we are drilling here...
Is English a pivot language? If I want to search for
answers on an Italian-biased subject, do I stick with
English, or give a try in Italian Wikipedia articles?
Are languages self-sufficient enough to answer most of
the GikiCLEF topics (monolingual), or having a
multilingual system pays off?
Where are the answers? In the text? In infoboxes? In
tables? Are categories useful?
CLEF 2009
Poster Boaster Session
Are Passages Enough? The
MIRACLE Team Participation in
QA@CLEF2009
María Teresa Vicente-Díez, César de PabloSánchez, Paloma Martínez, Julián Moreno
Schneider, Marta Garrote Salazar
(MIRACLE Team)
Universidad Carlos III de Madrid
Track: monolingual Spanish task at ResPubliQA
CLEF 2009
Poster Boaster Session
Main Features
2 runs
explore new domains, question types and
multilingual experiments
Adaptation to the new requirements
Indexes redesign
Passages fallback strategy
Domain-specific rules
Query generation adjustment
Temporal management addition
Acronyms mining functionality
Ranking module tunning
CLEF 2009
Poster Boaster Session
Experiments. Pending Issues
Results for submitted runs
Question
Analysis
Linguistic
Analysis
Question
Question
Classification
Information
Retrieval
Name
Right
Wrong
Overall
accuracy
mira091eses
161
339
0.32
mira092eses
147
352
0.29
Answer
Selection
Query
Generation
Answer
Filter
Information
Retrieval
Passage
Fallback
Strategy
Passage retrieval alternatives
Re-ranking
Filtering answers
Collection
Indexer
Linguistic
Analysis
Answer
Temporal-constraints
integration
Offline
Operations
Document
Collection
Answer selection helps?
Document
Index
System evolution
Configuration including answer filtering (BL+AF)
CLEF 2009
Poster Boaster Session
Domain independent
Multilingual approach
Evaluation
The UPC experience in Qast 2009
Pere R. Comas and Jordi Turmo
Technical University of Catalonia (UPC)
QA – QAst Track
CLEF 2009
Poster Boaster Session
Contents
System Architecture
Description of Modules
Partial Evaluation
Insights on NE recognition on Speech
QAst Evaluation Results
CLEF 2009
Poster Boaster Session
It has colorful charts
CLEF 2009
Poster Boaster Session
TIA-INAOE'S PARTICIPATION
AT IMAGECLEF 2009
(LARGE-SCALE PHOTOANNOTATION)
Hugo Jair Escalante
LABORATORIO DE
TECNOLOGÍAS DEL
LENGUAJE
hugojair@inaoep.mx
Track: ImageCLEF Photo Annotation
CLEF 2009
Poster Boaster Session
INSTITUTO NACIONAL DE
ASTROFÍSICA,
ÓPTICA Y ELECTRÓNICA
KNN-BASED IMAGE ANNOTATION

A score is assigned to the labels from the KNN
images to a test image:
 (liT ) =  r  wr (liT )   a  wa (liT )   s  ws (liT )
CLEF 2009
Poster Boaster Session
LABELING REFINEMENT WITH COOCCURENCES

The labels for the image are selected with an
energy-based model that attempts to maximize
the semantic cohesion of labels
No_Visual_Season
Outdoor
Day
No_Persons
Partly_Blurred
Neutral_Illumination
CLEF 2009
Poster Boaster Session
MIRACLE (FI) at ImageCLEFphoto
2009
R. Granados1, X. Benavent2, R. Agerri1,
Ana García-Serrano3, J.M. Goñi1, J.
Gomar2, E. de Ves2, J. Domingo2, G. Ayala2
1
Universidad Politécnica de Madrid, UPM
2 Universidad de Valencia
3 Universidad Nacional de Educación a
Distancia, UNED
Track: ImageCLEFphoto
CLEF 2009
Poster Boaster Session
Process Overview
WORD
(IDRA) TEXT-BASED RETRIEVAL
PDF
TXT
Text
Extractor
XML Format +
Selection
PreProcess
Collections/
captions
BELGA
CSV table
INDEX
Index
VIEW
IDRA
Index
Textual
Results
MySQL
table
MERGING
(results list)
Search
Prepare
Lists
ENRICH
NER tagger
QUERIES file
construction
EQUImerge
Generat
e Lists
Collections/
Topics
CONTENT-BASED
Collections
/ Imges
RETRIEVAL
(results list)
BELGA
(results list)
CLEF 2009
Poster Boaster Session
Merging algorithms (ImageCLEFphoto 2009)
MirFI1
MirFI2
80
30
20
average
best
MirFI1
MirFI2
MirFI3
MirFI4
MirFI5
40
MirFI5
average
MirFI4
MirFI3
50
%
MirFI1
MirFI2
60
best
70
10
0
MAP
R-Prec
Metrics
CLEF 2009
Poster Boaster Session
CR@10
MirFI5
average
90
MirFI3
MirFI4
best
CBIR module re-ranks obtained results from Textual one, using visual features
extraction and comparation techniques.
 ENRICH

enrichs a primary results list (Textual) using a support one (Visual)

if a concrete result appears in both T and V lists, its relevance value
is increased as follow:
 EQUImerge

from a cluster-level results list, representatives results from each one
of the different clusters are selected to build a new topic-level results
list with diversity
The University of Glasgow at
ImageClefPhoto 2009
G. Zuccon, T. Leelanupab, A. Goyal,
M. Halvey, P. Punitha, J. M. Jose
Multimedia Information Retrieval, Uni. of Glasgow
Track(s): ImageClef@CLEF
CLEF 2009
CLEF
2009
Poster Boaster
Session
Poster Boaster Session
Task: Promote Diversity in Image Ranking
Textual diversity
1) MMR
2) Semantic Clustering
1.1) Tackle problem of non- optimal ranking due to
initial bad document selection.
1.2) Incorporate clustering with MMR re-ranking
3) QPRP
Visual diversity
4) FA and Bi-Clustering
CLEF 2009
Poster Boaster Session
Results
Experimental results are very promising, where our
approaches compared against state of the art.
Our runs performance based on text diversification
outperforms other participants’ based on text features
only.
CLEF 2009
Poster Boaster Session
IAM@ImageCLEF 2009
Jonathon Hare, David Dupplaw and
Paul Lewis
Intelligence Agents Multimedia Group
School of Electronics and Computer Science
University of Southampton
Track(s): ImageCLEF Photo Retrieval & Photo Annotation
CLEF 2009
CLEF
2009
Poster Boaster
Session
Poster Boaster Session
Photo Retrieval: Promote Diversity
Challenge: Diversify image search results
Research question: Can we use image
features to diversify result sets?
How?
Start with a sound text retrieval
methodology!
Then add image features to re-order results
Novel maximal-distance ranking function
with powerful image features
CLEF 2009
Poster Boaster Session
Photo Retrieval: Promote Diversity
Results:
Text based retrieval worked rather well!
Image-feature based diversification works
But, at the cost of precision, with the current technique
Future Work:
Modify re-ranking algorithm to improve accuracy
Build into a real retrieval system
Thanks to: The EU for funding LivingKnowledge, which
is investigating bias and diversity in search.
CLEF 2009
Poster Boaster Session
THE SAIAPR TC12
BENCHMARK
http://imageclef.org/SIAPRdata
Hugo Jair Escalante
LABORATORIO DE
TECNOLOGÍAS DEL
LENGUAJE
hugojair@inaoep.mx
CLEF 2009
Poster Boaster Session
INSTITUTO NACIONAL DE
ASTROFÍSICA,
ÓPTICA Y ELECTRÓNICA
* In collaboration with Michael Grubinger
EXTENDING THE IAPR TC12
BENCHMARK

We manually segmented and annotated every
image in the IAPR TC12 collection with the goal
of providing ground-truth data for the evaluation
of image annotation methods
CLEF 2009
Poster Boaster Session
THE RESOURCE

The annotation vocabulary is organized into a
conceptual hierarchy
Landscape
Sky
Sky-blue
Sky-light
Sky-night
Sky-red

Cloud
Ground
Rock
Sun
Water
Arctic
Waterreflection
Waves
Glacier
Ice
Snow
Lake
Ocean
River
Swimmingpool
Waterfall
We provide:




Beach
Sand-beach
Shore
Island
Vegetation
Mountain
Flowerbed
Flower
Fruit
Apple
Grapes
Orange
Strawberry
Cactus
Grass
Mushroom
Plant
Leaf
Trees
Bush
Palm
Tree
Branch
Trunk
segmentation masks for 20,000 images,
labels 99,535 regions
visual features for 99,535 regions
spatial relationships among regions in the 20,000
images
CLEF 2009
Poster Boaster Session
Hill
Volcano
Evaluating Fusion Techniques
at Different Domains at
ImageCLEF Subtasks
Sergio Navarro, Rafael Muñoz,
Fernando Llopis
(University of Alicante)
Track(s): IMAGE@CLEF
Photo Retrieval, WikipediaMM, Medical Retrieval
CLEF 2009
CLEF
2009
Poster Boaster
Session
Poster Boaster Session
Objective: To evaluate ...
Multimodal Local Context Analysis
(MMLCA) evaluation on new collections.
Improved version of Multimodal Reranking
TF-IDF (MMRR TF-IDF)
Subquery Generation Module for improving
diversity and precision of the results.
Non-medical terms filtering: avoids the use
of non medical terms returned by the local
expansion technique used.
CLEF 2009
Poster Boaster Session
Concluding Results
MMLCA confirmed its better performance for the two
generic domain collections in comparison with other local
expansion techniques.
Subquery Generation Module demonstrated to be
specially effective when it is combined with Probabilistic
Relevance Feedback (PRF). (5th best textual run in
Photo Retrieval task).
A term coocurrence based technique like LCA does not
work properly in medical domain collections. A deep
understanding of the reasons could be a key for
future improvements.
CLEF 2009
Poster Boaster Session
i-score, Image – Semantic and
COntent based Retrieval system
Giannis Boutsis, Theodore Kalamboukis
Information Processing Laboratory
Department of Informatics
Athens University of Economics and Business
Track(s): Image@CLEF
CLEF 2009
Poster Boaster Session
CLEF 2009
Poster Boaster Session
ImageCLEF 2009
Removal of duplicate records (captions) 39,310 Discreate
documents remained
Stopword removal: Lucene + {caption, figure, image}
Stemming : Porter’s algorithm
ColorHistogram (0.3)
ColorLayout (0.3)
EdgeHistogram (1.0)
1 p
visual _ SCORE (i)   visual _ sim (i, ik )
p k 1
mixed _ SCORE (i)  0,8 * semantic _ SCORE (i)  0.2 * visual _ SCORE (i)
iS
CLEF 2009
Poster Boaster Session
Concluding Remarks
Visual and textual features should be used together for
information retrieval whenever they are both available to
allow optimal access to varied multimedia sources.
CLEF 2009
Poster Boaster Session
Topological Localization of
Mobile Robots using
Probabilistic Support Vector
Classification
Yan Gao, Yiqun Li
Department of Computer Vision and Image
Understanding, Institute for Infocomm
Research, Singapore
Track(s): RobotVision@CLEF
CLEF 2009
Poster Boaster Session
System Description
Feature extraction
Normalized Gaussian derivatives on the L component of
the LAB color space (Lx,Ly,Lxx,Lyy,Lxy)
Codebook k = 32
three-tier spatial pyramid
The classifier -- Probabilistic estimate for multi-class support
vector classification
Handling the unknown class
y=UK if pδ < T
Exploiting continuity of the sequence
Find the most frequent value in the past h frames
CLEF 2009
Poster Boaster Session
Experiments and Results
By incorporating the
prediction of the unknown
class based on the
probability estimates the
performance improves from
a score of 700 to 784, or
12%.
By applying a smoothing
procedure to exploit
continuity of the sequence,
the performance further
improves from 784 to
884.5, or 12.8%.
CLEF 2009
Poster Boaster Session
University of Amsterdam’s
Visual Concept Detection System
Koen van de Sande
Theo Gevers
Arnold Smeulders
Track: ImageCLEF
Large-Scale Visual Concept Detection and Annotation
CLEF 2009
CLEF
2009
Poster Boaster
Session
Poster Boaster Session
Automatic concept annotation
Given an image…
… is some concept is present in that image?
Examples:
Flower
Sunset
Outdoor
Single Person
Landscape
Sunny
CLEF 2009
CLEF
2009
Poster Boaster
Session
Poster Boaster Session
Ingredients
Keypoints
Annotated data
Color
Lowest equal error rate
40 out of 53 concepts
Bag-of-visual-words
Clouds
CLEF 2009
Poster Boaster Session
Sports are difficult
Come see the poster!
CLEF 2009
http://www.colordescriptors.com
Poster BoasterVisit
Session
for color descriptor software
Non-parametric Density
Estimation Algorithms
Ainhoa Llorente, Suzanne Little,
Stefan Rüger
Knowledge Media Institute
The Open University, UK
Track: ImageCLEF@CLEF
Photo Annotation
CLEF 2009
Poster Boaster Session
Architecture
CLEF 2009
Poster Boaster Session
Results
MMIS 33 2 1245434554581.txt: Weighted Global Features
MMIS 33 2 1245586552541.txt: Keyword Correlation
MMIS 33 2 1245611281967.txt: Normalized Google Distance
MMIS 33 2 1245674693001.txt: Adapted LESK (WordNet)
MMIS 33 2 1245601239738.txt: Wikipedia Link-based Measure
CLEF 2009
Poster Boaster Session
XRCE participation in Large Scale
Visual Concept Detection and
Annotation Task
Gabriela Csurka and Yan Liu
Xerox Research
Center Europe
6 chemin de Maupertuis
38240 Meylan, France
CLEF 2009
Poster Boaster Session
Generic Visual Categorization (GVC)
Patch
detection
Feature
extraction
+0.1
x = -1.5
…
-0.5
Visual
dictionary
High level
features
Classification
100
90
80
70
60
50
40
30
20
10
10
20
30
40
50
60
70
80
90
100
Patch detection: regular grid extracted at multiple scales
Feature extraction: SIFT and local color statistics
Visual dictionary: Gaussian Mixture Models
High level image representation: Fisher Vectors and Image
GMMs
Learning and Classification:
Non linear discriminative classifiers with late (score)
combination.
Post-processing to ensure constraints of the ontology.
CLEF 2009
Poster Boaster Session
Results
CLEF 2009
Poster Boaster Session
SZTAKI @ ImageCLEF 2009
Bálint Daróczy
joint work with
András A. Benczú́r, Zsolt Fekete, Dávid Nemeskey,
Istvá́n Petrá́s, Dá́vid Sikló́si, Zsuzsa Weiner
Data Mining and Web Search Group
Computer and Automation Research Institute
Hungarian Academy of Sciences
Text Based Image Retrieval
• Unstructured text, lots of text data cleaning
• Okapi BM25
• Thesaurus
• KL dissimilarity to increase cluster recall
• @WikiMM09
- text + Theasurus + image MAP 0.1699
- pure text
MAP 0.1583
Image Based Retrieval and Classification
• Segmentation (colour , shape and texture descriptors)
• SIFT descriptor (PhotoAnn)
• Logistic regression based on Fisher kernel (PhotoAnn)
•@PhotoAnn09 EER 0.291718 AUC 0.773133
Thank you!
UPMC/LIP6 at ImageCLEFannotation
2009: Large Scale Visual Concept
Detection and Annotation
Ali Fakeri-Tabrizi, Sabrina Tollari,
Ludovic Denoyer, Patrick Gallinari
(Université Pierre et Marie CURIE – Paris 6
UMR CNRS 7606 – LIP6, France)
Track : ImageCLEF
ANR-06-MDCA-002 AVEIR
CLEF 2009
Poster Boaster Session
VCDT – Visual Concept Detection Task
Automatically extracting visual concepts
Visual descriptors
Segmentation
HSV
• Use the relations between concepts
Detect the exclusive concepts
Co-occurrence matrix : “day” or “night”
Filter the classification scores using an optimism rate
Support Vector Machine with a convenient loss function
Baseline
CLEF 2009
Poster Boaster Session
VCDT – Visual Concept Detection Task
Automatically extracting visual concepts
Graph Classification
Each node represents an image
Each edge is weighted by the similarity between each
pair of images
Propagate the label of each node trough the graph
We have also participated in the AVEIR consortium runs
ANR-06-MDCA-002 AVEIR
CLEF 2009
Poster Boaster Session
Semantic Inter-media Image
Retrieval in Photographic
Collections
Osama El Demerdash, Leila Kosseim & Sabine
Bergler
CLaC Lab, Concordia University, Montreal
CLEF 2009
Poster Boaster Session
Overview
CLEF 2009
Poster Boaster Session
Results
Results on both IAPR-TC12 and Belga
Precision increases with Recall till 100 ?!!
CLEF 2009
Poster Boaster Session
A particle-filter-based self-localization
method using invariant features as
visual information
Jesús Martínez-Gómez
Alejandro Jiménez-Picazo
Ismael García-Varea
SIMD group (Intelligent system and data mining)
University of Castilla-La Mancha
Track: RobotVision@ImageCLEF
CLEF 2009
Poster Boaster Session
SIMD Approach
Obligatory track
Multi-classifier using
SIFT
Each test image is matched with all the training images
Additional image processing to detect natural landmarks
Hough transform with lines and squares detection
Optional track
Particle-filter-based localization method using:
SIFT for the update phase
Training images are processed offline to extract their points
Additional image processing
Population initialization if the system becomes unstable
The stability of the system is estimated
Frames are not classified when the uncertainty about the robot’s
pose is high
The complete robot’s pose can be obtained
CLEF 2009
Poster Boaster Session
Results for the tracks
Final Score Correctly Classified
Misclassified
Not Classified
Position
684
10th
217
1st
*** Obligatory Track ***
511.0
676
330
*** Optional Track ***
916.5
1072
311
CLEF 2009
Poster Boaster Session
Patent Retrieval Experiments in
the Context of the CLEF IP
Track 2009
Daniela Becks, Christa WomserHacker, Thomas Mandl, Ralph Kölle
(University of Hildesheim)
Track(s): IP@CLEF
CLEF 2009
Poster Boaster Session
Questions to be investigated
Does a statistical approach work well in the
patent domain?
How does the implemented stemmer influence
the retrieval process?
Do we need to remove stopwords?
CLEF 2009
Poster Boaster Session
Our approach
System based on Lucene
Monolingual runs (post runs) for German and English
Simple retrieval approach
Stopword removal
Stemming
Indexing
Term queries
CLEF 2009
Poster Boaster Session
Search Log Analysis at the
University of Sunderland
Michael Oakes, Yan Xu
(University of Sunderland)
Track(s): LADS@CLEF
CLEF 2009
Poster Boaster Session
Search Engine based on Query Logs
Instead of text documents being indexed
according to their content, they are indexed
according to the search terms previous users
have used in finding them.
Text
Query
Associated
keywords
Match
Ranked
Sessions Search Engine
CLEF 2009
Poster Boaster Session
Previously
Downloaded
URLs
Search
logs
Automatic Language Identification
Frequency of trigrams: sequences of 3
adjacent characters
Most searches are in English
Most searches are in only one language
Usually the query language is the same as the
interface language
Latin is similar to Italian
CLEF 2009
Poster Boaster Session
Morphological acquisition by
Formal Analogy
Jean-François Lavallée and
Philippe Langlais
University of Montreal
Track(s): Morpho Challenge@CLEF
CLEF 2009
Poster Boaster Session
Overview
Task : Identify morphological structure of
words in an unsupervised manner.
Method: Use analogies to find morphologically
related words.
Analogy: unreadable is to read what undoable
is to do
Result: Good results for 4 out of 5 langages
CLEF 2009
Poster Boaster Session
Schema and results
CLEF 2009
Poster Boaster Session
PROMODES – A probabilistic
generative model for word
decomposition
Sebastian Spiegler, Bruno Golénia, Peter Flach
Computer Science Department
University of Bristol, UK
Track: Morpho Challenge 2009
CLEF 2009
Poster Boaster Session
PROMODES – A probabilistic generative model for
word decomposition
Finding the best
segmentation
of a given
word
Competitive
results
in Morpho
Challenge 2009
Objective
Results
Model
Application
CLEF 2009
Poster Boaster Session
Based on hidden
boundary positions
and resulting
letter transitions
Languageindependent,
for fusional and
agglutinative languages
Chemnitz Retrieval Group:
Xtrieval Framework @ CLEF 2009
Jens Kürsten, Thomas Wilhelm,
Maximilian Eibl
(Chemnitz University of Technology)
Track(s): TEL@CLEF, Grid@CLEF, VideoCLEF,
ImageCLEFPhoto
CLEF 2009
Poster Boaster Session
Tracks & Technologies (1)
TEL@CLEF
Xtrieval framework with different IR cores
Multilingual approach using multiple indexes
Topic translation with Google’s Language API
Grid@CLEF
Implementation of CIRCO in Xtrieval
Comparing and combining different IR models
CLEF 2009
Poster Boaster Session
Tracks & Technologies (2)
VideoCLEF
Classification as IR task with Xtrieval
Fixing number of assigned Docs per Label
ImageCLEFPhoto
Simple approach without taking into account
the diversity of the topic
Xtrieval Framework using Lucene or Lemur
CLEF 2009
Poster Boaster Session
CACAO a Multilingual Interface to
Library Catalogues
Alessio Bosca, Luca Dini
alessio.bosca@celi.it, dini@celi.it
TEL@CLEF
CLEF 2009
Poster Boaster Session
What is CACAO?
CACAO offers an innovative
approach for accessing,
understanding and navigating
multilingual textual content in
digital libraries and Online Public
Access Catalogues (OPACs).
Linguistic agnostic
approach. Resources
for different
languages
(dictionaries,
thesauri,..) included
via Web Services
CLEF 2009
Poster Boaster Session
CACAO Suite
Different panels allows
librarian to mange the
different modules, view
logs and perform off-line
tasks/updates.
CLIR
FEATURES:
Multi-words,
Named Entities
Recognition,
Terms
Disambiguation,
Faceted Search..
CLEF 2009
Poster Boaster Session
MultiMatch:
Access to Cultural Heritage in
a MultiModal way
Giuseppe Amato, Pasquale Savino, Franca Debole
ISTI- CNR (Italy)
CLEF 2009
Poster Boaster Session
MultiMatch: here it is
MultiMatch is a Web search engine
specialized in the cultural heritage domain:
Queries can be expressed in multiple
languages
Multilingual and multimedia retrieval
Access to multiple sources of information
(e.g. museums, cultural institutions, CH
educational sites, tourist information portals)
CLEF 2009
Poster Boaster Session
Web from:
Museums
Libraries
Archives
Newspapers
News agencies
Personal Pages
Blogs
Van Gogh
Museum
Museums
Databases
National Gallery
Image
Similarity
Full
Text
Media
Repository
CLEF 2009
Poster Boaster Session
Demo: Automatic Langauge
Identification Using Trigram
Frequencies
Michael Oakes, Yan Xu
(University of Sunderland)
Track(s): LADS@CLEF
CLEF 2009
Poster Boaster Session
The 5 constituent trigrams in “_KATZE_”
Trigram
_KA
KAT
ATZ
TZE
English
1.23 E-5 4.72 E-7 4.72 E-7 3.49 E-5 1.70 E-5 1.63 E27
French
5.00 E-7 5.00 E-7 5.00 E-6 5.00 E-7 1.01 E-5 6.31 E32
German
5.64 E-4 1.41 E-4 1.41 E-4 1.41 E-4 1.12 E-4 1.77 E19
CLEF 2009
Poster Boaster Session
ZE_
Overall
Probability
Automatic Language Identification
Frequency of trigrams: sequences of 3
adjacent characters
Most searches are in English
Most searches are in only one language
Usually the query language is the same as the
interface language
Latin is similar to Italian
CLEF 2009
Poster Boaster Session
The University of Glasgow at
ImageClef 2009 Robot Vision
Task
Yue Feng, Martin Halvey and Joemon
M. Jose
Multimedia Information Retrieval, Uni. of Glasgow
Track(s): ImageClef@CLEF
CLEF 2009
Poster Boaster Session
Task: determine topological location of
mobile robots
Proposed Approaches - combine 2 main methods, from
coarse to fine procedures, plus illumination filter
1) POI matching in test and training frames to coarse
determination
2) Rule based reasoning to refine results
Two sets of data used for calculation
1) All frames in training and test set
2) First frame out of every five continuous frames
Total three runs of submission
CLEF 2009
Poster Boaster Session
Results
Our run ranked the first, relative to other participants’
Algorithm based on point matching, rule based
reasoning, and illumination filters applied on every
frames is effective to identify location of robot.
CLEF 2009
Poster Boaster Session
This is
THE END
Enjoy your lunch and
See you in the Pool Rooms for
The Poster Session
CLEF 2009
Poster Boaster Session