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Extracting an Ontology of Portrayable Objects from WordNet

This project aims to create a large-scale image ontology using WordNet lexical resources and web-based image mining for object recognition. By extracting portrayable objects from the top-level ontology, the system uses clustering and classification techniques for visual feature analysis. The focus is on simplifying connections and selecting relevant branches to build a comprehensive ontology. Additionally, the system utilizes a web-image search engine for indexing, clustering, and visualization of clusters. Future work includes face detection, cluster classification, and introducing new connections based on vision principles and co-occurrence rules.

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Extracting an Ontology of Portrayable Objects from WordNet

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  1. 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) BP6, 18 Route du Panorama 92265, Fontenay aux Roses, France sveta_zinger@yahoo.com, {milletc,mathieub,grefenstetteg,hedep,moellicp}@zoe.cea.fr

  2. Goal:creation of a large-scale image ontology • WordNet lexical resourses • Image collections acquisition through web-based image mining

  3. WordNet lexical resources basis of ontology list of portrayable objects Building a large-scale image ontology for object recognition:

  4. visual features semantic filtering clustering classification web-based image mining large-scale visual dictionary Building a large-scale image ontology for object recognition:

  5. ENTITY has a distinct separate existence (living or nonliving) OBJECT physical object (a tangible a visible entity) object  living thing  life  wildlife object  living thing  plant … tree  tree of knowledge object  artifact  creation  classic deleted Pruning approach to WordNet simplifying connections selecting branches

  6. ENTITY 102 nodes in total object living thing natural object artifact floater organism celestial body article commodity rock consumer goods Extraction from top-level ontology of portrayable objects

  7. web-image search engine (Alltheweb) indexing (PIRIA – LIC2M) clustering (shared nearest neighbor) visualisation of clusters VIKA (Visual Kataloguer)

  8. queries to the web (e.g. google image) List of portrayable objects (24000 items) VIKA (Visual Kataloguer)

  9. word identifying portrayable object + upper node Example of queries: Japanese pagoda • kino tree • red sandalwood tree • carib wood tree • Japanese pagoda tree • palm tree • ... buildings Japanese pagoda tree trees Composition of queries:

  10. VIKA (Visual Kataloguer)

  11. query « chair» Web-image search at present Desired results

  12. 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

  13. Future work Vision principles (scale)

  14. Future work Co-occurrence rules

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