Interactive Feature Growing for Accurate Object Detection in Megapixel Images

Julius Schöning, Patrick Faion, Gunther Heidemann

Abstract: Automatic object detection in megapixel images is quite inaccurate and a time and memory expensive task, even with feature detectors and descriptors like SIFT, SURF, ORB, and KAZE. In this paper we propose an interactive feature growing process, which draws on the efficiency of the users’ visual system. The performance of the visual system in search tasks is not affected by the pixel density, so the users’ gazes are used to boost feature extraction for object detection. Experimental tests of the interactive feature growing process show an increase of processing speed by $$50\,\%$$ for object detection in 20 megapixel scenes at an object detection rate of $$95\,\%$$ . Based on this method, we discuss the prospects of interactive features, possible use cases and further developments.