ITM Web Conf.
Volume 12, 2017The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
|Number of page(s)||4|
|Section||Session 5: Information Processing Methods and Techniques|
|Published online||05 September 2017|
A Replacement Algorithm of Non-Maximum Suppression Base on Graph Clustering
National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, China
Non-maximum suppression is an important step in many object detection and object counting algorithms. In contrast with the extensive studies of object detection, NMS method has not caused too much attention. Although traditional NMS method has demonstrated promising performance in detection tasks, we observe that it is a hard decision approach, which only uses the confidential scores and Intersection-over-Unions (IoUs) to discard proposals. By this way, NMS method would keep many false proposals whose IoU with the ground truth proposal is smaller than the threshold, which indicates that NMS may not suitable for counting the object number in images. To eliminate the limitation on object counting task, we propose a novel algorithm base on graph clustering to replace the NMS method in this paper. Experiments on faster-rcnn and SSD show that our algorithm achieves better performance than that of NMS on the object counting task.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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