Category independent object proposals using quantum superposition
Junaid Malik, Çağlar Aytekin, Moncef Gabbouj · 2017
Object proposals improve the efficiency of object detection by providing probable locations of objects in an image. Most of the state-of-the-art object proposal methods employ a supervised approach and learn object features from ground truth annotations. We present a novel unsupervised approach for generating object proposals that is based on the human visual system and quantum mechanical principles. Despite of being devoid of any learnt priors pertaining to objects in images, the proposed method is shown to yield competitive results with supervised approaches.