Describing Images Using Qualitative Models and Description Logics

Abstract

Our approach describes any digital image qualitatively by detecting regions/objects inside it and describing their visual characteristics (shape and colour) and their spatial characteristics (orientation and topology) by means of qualitative models. The description obtained is translated into a description logic (DL) based ontology, which gives a formal and explicit meaning to the qualitative tags representing the visual features of the objects in the image and the spatial relations between them. For any image, our approach obtains a set of individuals that are classified using a DL reasoner according to the descriptions of our ontology.

Keywords:

• qualitative shape
• qualitative colours
• qualitative orientation
• topology
• ontologies
• computer vision

Notes

¹More details in: http://people.cs.uchicago.edu~pff/segment/

²Ontology Web Language: http://www.w3.org/TR/owl2-syntax/

³A _i TC _i denotes the angle or the type of curvature that occurs at the point P _i .

⁴See the CSS3 specification from the W3C (http://www.w3.org/TR/css3-color/#hsl-color)

⁵Available at: http://krono.act.uji.es/people/Ernesto/qimage-ontology/

⁶Protégé: http://protege.stanford.edu

⁷ http://hermit-reasoner.com/

⁸We have created this knowledge layer from scratch. In the near future it would be interesting to integrate our reference conceptualization with standards such as MPEG-7 for which an OWL ontology is already available (Hunter, 2001; Hunter, 2006), or top-level ontologies such as DOLCE (Gangemi et al., 2002).

⁹It is well known that the use of nominals makes reasoning more difficult (Tobies, 2001); however, in this case each image contains a relatively small number of individuals

¹⁰FaCT++: http://owl.man.ac.uk/factplusplus/

¹¹Pellet: http://clarkparsia.com/pellet/