Tropical cyclones objection detection based on faster R-CNN and infrared satellite cloud images

Chang-Jiang Zhang; Qi Luo

doi:10.1117/12.2513984

29 October 2018 Tropical cyclones objection detection based on faster R-CNN and infrared satellite cloud images

Chang-Jiang Zhang, Qi Luo

Proceedings Volume 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence; 108360G (2018) https://doi.org/10.1117/12.2513984
Event: 2018 International Conference on Image, Video Processing and Artificial Intelligence, 2018, Shanghai, China

Abstract

Since the labels of training samples are related to bags not instances, the multiple instance learning (MIL) is a special ambiguous learning paradigm. In this paper, we propose a novel bag space (BS) construction and extreme learning machine (ELM) combination method named BS_ELM for MIL, which can capture the bag structure and use the efficiency of ELM. Firstly, sparse subspace clustering is performed to obtain the cluster centers and a new bag space is constructed. Then ELM is used to classify bags in the new space. Experiments on data sets demonstrate the utility and efficiency of the proposed approach as compared to the other state-of-the-art MIL algorithms.

Citation Download Citation

Chang-Jiang Zhang and Qi Luo "Tropical cyclones objection detection based on faster R-CNN and infrared satellite cloud images", Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 108360G (29 October 2018); https://doi.org/10.1117/12.2513984

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