Deep Scene Image Classification with the MFAFVNet
Deep Scene Image Classification with the MFAFVNet
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DOI：
10.1109/iccv.2017.613
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发表时间：
2017-10
期刊：
影响因子：
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通讯作者：
Yunsheng Li;Mandar Dixit;N. Vasconcelos
中科院分区：
文献类型：
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作者：
Yunsheng Li;Mandar Dixit;N. Vasconcelos
考虑将为对象识别训练的深度卷积网络转移到场景图像分类任务的问题。提出了一种嵌入式实现最近提出的混合因子分析Fisher向量（MFA-FV）。这使得能够设计可以以端到端方式训练的网络架构MFAFVNet。新架构涉及MFA-FV层的设计，该层通过网络计算和正则化的组合实现MFA-FV的统计正确版本。与以前的Fisher向量神经实现相比，MFAFVNet依赖于更强大的统计模型和更准确的实现。与以前的非嵌入式模型相比，MFAFVNet依赖于最先进的模型，现在嵌入到CNN中。这使得端到端的培训。实验结果表明，MFAFVNet具有较好的场景分类性能.
The problem of transferring a deep convolutional network trained for object recognition to the task of scene image classification is considered. An embedded implementation of the recently proposed mixture of factor analyzers Fisher vector (MFA-FV) is proposed. This enables the design of a network architecture, the MFAFVNet, that can be trained in an end to end manner. The new architecture involves the design of a MFA-FV layer that implements a statistically correct version of the MFA-FV, through a combination of network computations and regularization. When compared to previous neural implementations of Fisher vectors, the MFAFVNet relies on a more powerful statistical model and a more accurate implementation. When compared to previous non-embedded models, the MFAFVNet relies on a state of the art model, which is now embedded into a CNN. This enables end to end training. Experiments show that the MFAFVNet has state of the art performance on scene classification.