基于FCN的图像感兴趣区域提取与细粒度分类的研究
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摘要
近年来基于深度学习的细粒度分类是研究的热点,细粒度分类的主要方法是先找出分类对象再分类。找出分类对象的方法中主要分为两种:强监督与弱监督,强监督需要使用昂贵的人工标签,为了减少人工标注成本,提出一种基于FCN的图像感兴趣区域的分割与提取,并利用分割的图像进一步训练网络提高正确率。
In recent years, fine-grained classification based on deep learning is a hot topic. The main method of fine-grained classification is to first find the classification object and then classify it. There are two main methods for finding classification objects: strong supervision and weak supervision. Strong supervision requires the use of expensive manual labels. In order to reduce the cost of manual labeling, mainly proposes a segmentation and extraction of image regions based on improved FCN. The method and use the segmented image to further train the network to improve the correct rate.
In recent years, fine-grained classification based on deep learning is a hot topic. The main method of fine-grained classification is to first find the classification object and then classify it. There are two main methods for finding classification objects: strong supervision and weak supervision. Strong supervision requires the use of expensive manual labels. In order to reduce the cost of manual labeling, mainly proposes a segmentation and extraction of image regions based on improved FCN. The method and use the segmented image to further train the network to improve the correct rate.
引文
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[3]M.-E. Nilsback,A. Zisserman. Automated Flower Classification over a Large Number of Classes. In Proceedings of the Indian Conference on Computer Vision. Graphics and Image Processing,2008.
[4]S. Maji,J. Kannala,E. Rahtu,M. Blaschko,A. Vedaldi.Fine-Grained Visual Classification of Aircraft. Technical Report,2013.
[5]T. Xiao,Y. Xu,K. Yang,J. Zhang,Y. Peng,Z. Zhang. The Application of Two-Level Attention Models in Deep Convolutional Neural Network for Fine-Grained Image Classification. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,Boston,MA,Jun. 2015:842-850.
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[7]T.Y. Lin,A. RoyChowdhury,S. Maji. Bilinear CNN Models for Fine-Grained Visual Recognition. In Proceedings of IEEE International Conference on Computer Vision,Sandiago,Chile,Dec. 2015:1449-1457.
[8]Shelhamer E,Long J,Darrell T. Fully Convolutional Networks for Semantic Segmentation[M]. IEEE Computer Society,2017.
[9]Rahman M A, Wang Y. Optimizing Intersection-Over-Union in Deep Neural Networks for Image Segmentation[M]. Advances in Visual Computing. Springer International Publishing,2016:234-244.
[10]X. Liu,T. Xia,J. Wang,Y. Q. Lin. Fully Convolutional Attention Localization Networks:Efficient Attention Localization for Fine-Grained Recognition. arXiv:1603.06765,2016.
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