Semi-autonomous evolution of object models for adaptive object recognition

The paper presents a semi-autonomous model evolution approach to object recognition under variable perceptual conditions. The approach assumes that (i) the system has to recognize objects on separate images of a sequence, and (ii) the images demonstrate the variability of conditions under which objects are perceived (gradual change in resolution, lighting, positioning). The adaptation of object models is executed due to perceived, over a sequence of images, variabilities of object characteristics. This adaptation involves (i) the application of learned models to the next image, (ii) the monitoring of recognition effectiveness of the models, and (iii) an activation of learning processes if needed (i.e., when the recognition effectiveness of the models decreases). Model adaptation (evolution) integrates recognition processes of computer vision with incremental knowledge acquisition processes of machine learning in a closed loop. The paper presents both an outline of the iterative evolution methodology and the investigation of an incremental model generalization approach using the example of a texture recognition problem. Experiments were run in a semi-autonomous mode where a teacher secured soundness behavior of the evolution system. The experiments are compared for three system configurations: (i) a one-level control structure, (ii) a two-level control structure, and (iii) a two-level control structure with data filtering. The obtained results are evaluated for system recognition effectiveness, recognition stability, and predictability of evolved models