Embedding high-level information into low level vision: Efficient object search in clutter

Ching Lik Teo, Austin Myers, Cornelia Fermüller, Yiannis Aloimonos

Abstract: The ability to search visually for objects of interest in cluttered environments is crucial for robots performing tasks in a multitude of environments. In this work, we propose a novel visual search algorithm that integrates high-level information of the target object - specifically its size and shape, with a recently introduced visual operator that rapidly clusters potential edges based on their coherence in belonging to a possible object. The output is a set of fixation points that indicate the potential location of the target object in the image. The proposed approach outperforms purely bottom-up approaches - saliency maps of Itti et al. [15], and kernel descriptors of Bo et al. [2], over two large datasets of objects in clutter collected using an RGB-Depth camera.