  We base our work on the teleosemantic modelling of concepts as abilities
implementing the distinct functions of recognition and classification.
Accordingly, we model two types of concepts - substance concepts suited for
object recognition exploiting visual properties, and classification concepts
suited for classification of substance concepts exploiting linguistically
grounded properties. The goal in this paper is to demonstrate that object
recognition can be construed as classification via visual properties, as
distinct from work in mainstream computer vision. Towards that, we present an
object recognition process based on Ranganathan's four-phased faceted knowledge
organization process, grounded in the teleosemantic distinctions of substance
concept and classification concept. We also briefly introduce the ongoing
project MultiMedia UKC, whose aim is to build an object recognition resource
following our proposed process
