A learning-based evolution of concept descriptions for an adaptive object recognition

Abstract

An approach is presented to the invariant recognition of objects under dynamic perceptual conditions. In this approach, images of a sequence are used to adapt object descriptions to perceived online variabilities of object characteristics. This adaptation is made possible by the closed-loop integration of recognition processes of computer vision together with an incremental machine learning process. The experiments presented were run for the texture recognition problem and were limited to a partially supervised evolution of concept descriptions (models) rather than utilizing a fully autonomous model evolution. Obtained results are evaluated using the criteria of system recognition effectiveness and recognition stability.<>