Object Localization Based on Proposal Fusion

Traditional regression framework of object locali-zation such as Overfeat often suffers from the problem of inaccurate scoring due to the separate scoring of classification network and regression network upon inconsistent regions. To tackle this problem, in this paper, we propose a novel object localization framework based on multiple complementary region proposal methods from the view of classification rather than regression. On top of our framework, we first combine multiple complementary region proposals during both training and testing as a means of data augmentation to generate more dense and reliable proposals for fusion, then achieve optimal compromise between complexity and efficiency through category clustering for bounding box sharing among similar categories, and finally propose a dense proposal fusion approach to merge dense region proposals near true object for fine-tuning of the final bounding box's coordinates and updating the confidence of fused proposals for final decision. Extensive experiments on the well-known large scale ILSVRC 2015 LOC dataset verify the effectiveness of our object localization framework.