Abstract
Recently, an overwhelming majority of object detection methods have focused on how to reduce the number of region proposals while keeping high object recall without consideration of category information. It may lead to a lot of false positives due to the interferences between categories especially when the number of categories is very large. To eliminate such interferences, we propose a novel category aggregation approach based upon our observation that more frequently detected categories around an object have the higher probabilities to be present in an image. After further exploiting the co-occurrence relationship between categories, we can determine the most possible categories for an image in advance. Thus, many false positives can be greatly filtered out before subsequent classification process. Our extensive experiments on the well-known ILSVRC 2015 detection dataset show that our approach can achieve 49.0% of mAP in the validation dataset and 45.36% of mAP in the test dataset ranked 5th in the ILSVRC 2015 detection task.
This work was supported by 863 Project (2014AA015202), National Nature Science Foundation of China (61572472), Beijing Natural Science Foundation (4152050) and Beijing Advanced Innovation Center for Imaging Technology (BAICIT-2016009).
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Li, L., Tang, S., Zhou, J., Wang, B., Tian, Q. (2016). Category Aggregation Among Region Proposals for Object Detection. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9917. Springer, Cham. https://doi.org/10.1007/978-3-319-48896-7_21
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