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Unsupervised Object Discovery for Instance Recognition
2018
2018 IEEE Winter Conference on Applications of Computer Vision (WACV)
Severe background clutter is challenging in many computer vision tasks, including large-scale image retrieval. Global descriptors, that are popular due to their memory and search efficiency, are especially prone to corruption by such a clutter. Eliminating the impact of the clutter on the image descriptor increases the chance of retrieving relevant images and prevents topic drift due to actually retrieving the clutter in the case of query expansion. In this work, we propose a novel salient
doi:10.1109/wacv.2018.00194
dblp:conf/wacv/SimeoniITAC18
fatcat:yqdibyg5tbdxnepmeuamtq2y6e