Ontologies for Object Recognition, 10-R9733Printer Friendly Version
Inclusive Dates: 07/01/07 – 11/20/07
Background - Object recognition is the method by which objects in an image are classified. In the past, object recognition has been very difficult for computers, despite being a task that is easily mastered by people. Traditional computer vision approaches to object recognition break down an image into smaller images or objects, with each represented by a set of features. Pattern recognition methods are then used to compare the unknown object features to a set of known objects. A common problem in pattern recognition is when multiple known objects are matched to the unknown object. In this case, because the feature set is limited, the unknown object does not uniquely map to a single known object. For past object recognition applications this limitation was not an issue because the number of known objects could be kept small. In addition, the image and objects of interests were well controlled, allowing a high probability of accurate recognition by traditional methods. However, future applications of object recognition will require methods that can operate in a dynamic environment with imperfect information. Object recognition methods will have to recognize a large number of objects that may not be uniquely defined by their features.
Approach - To achieve the goal of recognizing a large number of objects, contextual information and feature data must both be considered. For example, a book may not be uniquely defined by its features alone, but if a book were shown on a bookshelf in an image, that additional contextual information could be used to classify the object as a book. Software ontologies are ideal for incorporating context into object classification because they allow the efficient storage and reasoning of common sense knowledge that is required to define and use contextual information. The incorporation of software ontologies into object recognition is expected to vastly improve object recognition over traditional methods. To test this, simulated objects and their corresponding features will be classified by the new ontology method and by the traditional pattern recognition method. A simulation will be used to accelerate the development of the ontology-based object recognition. The simulation data will be based on a real-life problem of satellite imagery analysis so that the results will be credible, even though based on a simulation.
Accomplishments - Research has just begun for this project. A software plan that outlines the major functional requirements of the object recognition software has been developed.