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

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-classication 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.