Learning Deep Relationship for Object Detection

• XU Nuo

  National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences

• HUO Chunlei

  National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences

抄録

Object detection has been a hot topic of image processing, computer vision and pattern recognition. In recent years, training a model from labeled images using machine learning technique becomes popular. However, the relationship between training samples is usually ignored by existing approaches. To address this problem, a novel approach is proposed, which trains Siamese convolutional neural network on feature pairs and finely tunes the network driven by a small amount of training samples. Since the proposed method considers not only the discriminative information between objects and background, but also the relationship between intraclass features, it outperforms the state-of-arts on real images.

収録刊行物

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E101.D (1), 273-276, 2018

  一般社団法人 電子情報通信学会

キーワード

• object detection
• Siamese convolutional neural network
• remote sensing images
• relationship learning

詳細情報

• CRID: 1390001204380959616
• NII論文ID: 130006301141
• DOI: 10.1587/transinf.2017edl8131
• ISSN: 17451361
• ISSN: 09168532
• Web Site: https://www.jstage.jst.go.jp/article/transinf/E101.D/1/E101.D_2017EDL8131/_pdf
• 本文言語コード: en
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