Non-maximum Suppression for Object Detection by Passing Messages Between Windows

• Published in: Computer Vision -- ACCV 2014 Vol. 9003; pp. 290 - 306
• Main Authors: Rothe, Rasmus, Guillaumin, Matthieu, Van Gool, Luc
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2015; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Affinity Propagation Cluster; Artificial intelligence; Cluster Center; Image processing; Local Binary Pattern; Object Class; Object Detection; Pattern recognition
• ISBN: 3319168649; 9783319168647
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-16865-4_19

Non-maximum suppression (NMS) is a key post-processing step in many computer vision applications. In the context of object detection, it is used to transform a smooth response map that triggers many imprecise object window hypotheses in, ideally, a single bounding-box for each detected object. The most common approach for NMS for object detection is a greedy, locally optimal strategy with several hand-designed components (e.g., thresholds). Such a strategy inherently suffers from several shortcomings, such as the inability to detect nearby objects. In this paper, we try to alleviate these problems and explore a novel formulation of NMS as a well-defined clustering problem. Our method builds on the recent Affinity Propagation Clustering algorithm, which passes messages between data points to identify cluster exemplars. Contrary to the greedy approach, our method is solved globally and its parameters can be automatically learned from training data. In experiments, we show in two contexts – object class and generic object detection – that it provides a promising solution to the shortcomings of the greedy NMS.