Ontology-based inference system for adaptive object recognition

This paper presents a statistical ontology approach for adaptive object recognition in a situation-variant environment. We propose a context model based on statistical ontology that is concentrated on object recognition. Due to the effects of illumination on a supreme obstinate designing context-sensitive recognition system, we focused on designing a context-variant system using statistical ontology. Ontology, a collection of concepts and their interrelationships, provides an abstract view of an application domain. Researchers produce ontologies in order to understand and explain underlying principles and environmental factors. In this paper, we propose an ontology-based inference system for adaptive object recognition. The proposed method utilizes context ontology, context modeling, context adaptation, and context categorization to design the ontology based on illumination criteria for surveillance. After selecting the proper ontology domain, a set of actions is selected that produces better performance in that domain. We also carried out extensive experiments on these concepts in the area of object recognition in a dynamic changing environment, achieving enormous success that will enable us to proceed with our basic concepts.