TOWARD A VISUAL COGNITIVE SYSTEM USING ACTIVE TOP-DOWN SACCADIC CONTROL

Abstract

The saccadic selection of relevant visual input for preferential processing allows the efficient use of computational resources. Based on saccadic active human vision, we aim to develop a plausible saccade-based visual cognitive system for a humanoid robot. This paper presents two initial steps toward our objective by extending the saccade-based model of human memory called NIM 1 to a plausible model of natural visual classification. NIM builds feature-vector representations from selected local image samples and uses these to make memory-based decisions. As a first step, we adapt NIM to a straightforward saccade-based model for the classification of natural visual input called NIM-CLASS and evaluate the model in a face-classification experiment. As a second step, we aim to approach the interactive nature of human vision by extending NIM-CLASS to NIM-CLASS TD by adding active top-down saccadic control. We then assess to what extent top-down control enhances classification performance. The results show that the incorporation of top-down saccadic control benefits classification performance compared to the purely bottom-up control, reducing the amount of visual input required for correct classification. We conclude that NIM-CLASS TD may provide a fruitful basis for an active visual cognitive system in a humanoid robot that enables efficient visual processing.