Toward Composite Object Classification Using a Probabilistic Inference Engine

Abstract

This paper describes a new framework for classifying objects in still images using Description Logic reasoners. Unlike classical knowledge representation, features extracted from visual images are not always certain but rather ambiguous and probabilistic. To handle such uncertainty at the reasoning level, we employ the advantage of a probabilistic inference engine besides a classical reasoner, and design an image object ontology accordingly. The ontology defines composite objects in terms of basic objects, and basic objects in terms of visual features like shapes and colors. The proposed framework aims at improving on existing works in terms of both scalability and reusability. We demonstrate the performance of our object classification framework on a collection of car side images and compare to other approaches. Not only does our method show a distinctly better accuracy, but also each object classification result is equipped with a probability range.

This work is partially funded by the National Research University (NRU) project of Thailand Office for Higher Education Commission.