extracting an ontology of portrayable objects from wordnet
DESCRIPTION
Extracting an Ontology of Portrayable Objects from WordNet. S. Zinger, C. Millet, B. Mathieu, G. Grefenstette, P. Hède, P.-A. Moëllic. Atomic Energy Agency of France (CEA) LIC2M (Multilingual Multimedia Knowledge Engineering Laboratory) - PowerPoint PPT PresentationTRANSCRIPT
MUSCLE/ImageCLEF workshop 2005
Extracting an Ontology of Portrayable Objects from
WordNet
Atomic Energy Agency of France (CEA)LIC2M (Multilingual Multimedia Knowledge Engineering Laboratory)BP6, 18 Route du Panorama 92265, Fontenay aux Roses, France
[email protected], {milletc,mathieub,grefenstetteg,hedep,moellicp}@zoe.cea.fr
S. Zinger, C. Millet, B. Mathieu, G. Grefenstette, P. Hède, P.-A. Moëllic
2MUSCLE/ImageCLEF workshop
2005
Goal:creation of a large-scale image ontology
• WordNet lexical resourses
• Image collections acquisition through web-based image mining
3MUSCLE/ImageCLEF workshop
2005
Building a large-scale image ontology for object recognition:
list of portrayable objects
WordNet lexical resources
basis of ontology
4MUSCLE/ImageCLEF workshop
2005
Building a large-scale image ontology for object recognition:
visual features
semantic filtering
clustering
classification
web-based image mining
large-scale visual dictionary
5MUSCLE/ImageCLEF workshop
2005
Pruning approach to WordNetENTITY
has a distinct separate existence (living or nonliving)
OBJECT physical object (a tangible a visible entity)
simplifying connections
selecting branches
object living thing life wildlifeobject living thing plant … tree tree of knowledgeobject artifact creation classic
deleted
6MUSCLE/ImageCLEF workshop
2005
Extraction from top-level ontology of portrayable objects
ENTITY
object
living thing
natural object artifact floater
organism celestial body
rock
article commodity
consumer goods
102 nodes in total
7MUSCLE/ImageCLEF workshop
2005
VIKA (Visual Kataloguer)
indexing (PIRIA – LIC2M)
clustering (shared nearest neighbor)
visualisation of clusters
web-image search engine(Alltheweb)
8MUSCLE/ImageCLEF workshop
2005
List of portrayable objects(24000 items)
queries to the web (e.g. google image)
VIKA (Visual Kataloguer)
9MUSCLE/ImageCLEF workshop
2005
Composition of queries:
upper node +word identifying portrayable object
Example of queries:
• kino tree• red sandalwood tree • carib wood tree• Japanese pagoda tree• palm tree• ...
Japanese pagoda
buildings
Japanese pagoda tree
trees
10MUSCLE/ImageCLEF workshop
2005
VIKA (Visual Kataloguer)
11MUSCLE/ImageCLEF workshop
2005
Web-image search at present Desired results
query « chair»
12MUSCLE/ImageCLEF workshop
2005
Future work:• face detection (adaboost learning) – to filter web-image search results semantically (images of objects without people)
• testing VIKA system performances
• automatic cluster classification – ignoring irrelevant clusters
• introducing new connections to the ontology: vision principles (scale), co-occurrence rules
13MUSCLE/ImageCLEF workshop
2005
Future workVision principles (scale)
14MUSCLE/ImageCLEF workshop
2005
Future workCo-occurrence rules