An Evaluation of Perceptual Classification led by Cognitive Models in Traffic Scenes

Abstract

The objects extraction and recognition constitute the most important link in the image processing and understanding, and it cannot be achieved without a solid objects organization during the processing through the learning mechanisms. Most often, both the response time and the accuracy are undeniable criteria for applications in this field. Actually, a vision system need to take into consideration these criteria, either in the structural, the methodological or in the algorithmic aspect. Thus, we consider that the ontological study at the domain and task levels, in the vision systems, has become essential in order to provide a substantial assistance to the multitudes of applications in image processing. Concerning the domain knowledge, several patterns for structuring were proposed to improve the objects representation and organization, they often advocate the precision aspect on time and on effort devoted to the recognition. In practical terms, clustering methods only focus on the accuracy aspect within a category, without considering the recognition aspect [1]. Thus, we propose in this study a new procedure of object categorization, which uses, according to the expertise in the domain, a fit evaluation that is able to adjust the level of partitioning. As a result, this procedure will find a compromise between the accuracy on the categories and the reduction of the supplied effort in recognition.

Towards Domain Ontology Creation Based on a Taxonomy Structure in Computer Vision

“…We have proposed in [1] a measure noted Ep() for a partition  in the taxonomic tree. It is based on cue-validity, which expresses the non-uniformity of properties in categories.…”

“…We adopt a structuring method through image classification using a measuring function based on the cue-validity of attributes [1], which evaluates each partition and preserves the best according to certain criteria. Our function allows us to generate a tree structure (taxonomic tree) with several different levels from each other by their level of abstraction and precision.…”