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首页> 外文会议>International conference on pattern recognition and machine intelligence >An Approach of Transferring Pre-trained Deep Convolutional Neural Networks for Aerial Scene Classification
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An Approach of Transferring Pre-trained Deep Convolutional Neural Networks for Aerial Scene Classification

机译：一种将预训练的深度卷积神经网络转移到空中场景分类中的方法

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摘要

Feature selection or feature extraction plays a vital role in image classification task. Since the advent of deep learning methods, significant efforts have been given by researchers to obtain an optimal feature set of images for improving classification performance. Though several deep architectures of Convolutional Neural Networks (CNNs) have been successfully designed but training such deep architectures with small datasets like aerial scenes often leads to overfitting hence affects the classification accuracy. To tackle this issue in past few works, pre-trained CNNs are adopted as feature extractor where features are directly transferred to train only the classification layer for classifying images on the target dataset. In this work, an approach of feature extraction is proposed where both "multi-layer" and "multi-model" features are extracted from pre-trained CNNs. "Multi-layer" features are concatenation of features from multiple layers within a same CNN and "Multi-model" are concatenation of features from different CNN models. 'The concatenated features are further reduced with some method to obtain an optimal feature set.
机译：特征选择或特征提取在图像分类任务中起着至关重要的作用。自从深度学习方法问世以来，研究人员已经做出了巨大的努力来获得最佳的图像特征集，以改善分类性能。尽管已成功设计了几种卷积神经网络（CNN）的深层体系结构，但是使用诸如航空场景之类的小型数据集训练这种深层体系结构通常会导致过拟合，因此会影响分类准确性。为了在过去的几篇著作中解决此问题，采用了预训练的CNN作为特征提取器，在该提取器中，特征被直接转移以仅训练用于对目标数据集上的图像进行分类的分类层。在这项工作中，提出了一种特征提取方法，其中从预先训练的CNN中提取“多层”和“多模型”特征。 “多层”特征是同一CNN中多个图层的特征的串联，而“多模型”是不同CNN模型中的特征的串联。 '使用某种方法可以进一步减少级联特征，以获得最佳特征集。

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