1. Ontology Based Object Learning and Recognition PhD Defence 14/12/2005 Supervised by Monique Thonnat Nicolas MAILLOT Orion team INRIA Sophia Antipolis

15. Knowledge Acquisition Domain Expert Knowledge Acquisition Knowledge Base Knowledge acquisition guided by a visual concept ontology (i.e geometry, texture, color ) to describe the objects of the domain. Visual Concept Ontology

18. Knowledge Acquisition Texture Repartition Pattern Repetitive Random Regular Oriented Granulated Coarse Complex Visual concept ontology content: some texture concepts Based on cognitive experiments [Bhushan et al 97]

21. Knowledge Acquisition Each visual concept is associated with numerical features: Histograms Color Coherence Vectors [Pass96] Blue, Bright, Dark Color Gabor Features [Manjunath 96] Co-Occurrence Matrices Granulated, Smooth Texture SIFT Features [Lowe 99] Polygonal, Straight Shape Numerical Features Examples Visual Concept

23. Domain class hierarchy Subparts hierarchy Ontology driven description Image samples management Knowledge Acquisition

24. Poaceae Composition Link Specialization Link Pollen Grain Pori Non Apertured Pollen Cupressaceae Pori of Poaceae Pori of Parietaria Knowledge Base (18 domain classes + 17 visual concepts) Cytoplasm Of Cupressaceae Pollen with Pori Pollen with Pori and Colpi Apertured Pollen Parietaria Olea Colpi Colpi of Olea Knowledge Acquisition Context: Sensor: Microscope Magnification: 60 Dye: Fuchsin

39. Object Categorization Automatic Segmentation Feature Extraction Input Image Poaceae 0.63 Circular 0.5 Pink 0.8 Granulated 0.6 Object Categorization Visual Concept Detectors Mapping Knowledge Base

45. Results Composition Link Specialization Link Outdoor Scene Transport Vehicles Background Sky Aircraft Tarmac Grass Sea Car Motorbike Knowledge acquisition

46. Results Knowledge acquisition Uniform Bottom Green Grass Uniform Bottom Grey Black Tarmac Smooth Top Dark Light Blue Grey Sky Center Polygonal Motorbike Center Polygonal Car Center Polygonal Aircraft Pattern Position Geometry Brightness Hue

56. Proposed Approach Data Management Knowledge Base of Visual Concepts and Data Data Management Engine Interpretation Knowledge Base of Application Domain and Visual Concepts Interpretation Engine Program Supervision Library of vision programs Knowledge Base of Program Utilization Program Supervision Engine Current Image Interpretation Object Hypotheses Image Processing Request Numerical data Image description Visual Concept Ontology Cognitive vision platform [Hudelot 05]