Object Recognition as Classification via Visual Properties

The current work is based on the teleosemantic modelling of concepts as abilities implementing the distinct functions of recognition and classification. Accordingly, two types of concepts are modelled – 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, a novel object recognition process based on Ranganathan’s four-phased faceted knowledge organization process is presented, grounded in the teleosemantic distinctions of substance concept and classification concept. The ongoing project MultiMedia UKC, whose aim is to build an object recognition resource following the proposed process, is also briefly introduced.

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.