Explore the vast range of titles available.

使用机器学习进行遥感影像标注的一个重要前提是有足够的训练样本,而样本的标注是非常耗时的。本文采用了域适应的方法来解决遥感影像场景分类中小样本量的无监督学习问题,提出了结合对抗网络与辅助任务的遥感影像域适应方法。首先建立了基于深度卷积神经网络的遥感影像分类框架;其次,为了学习到域不变特征,在标签分类器的基础上增加域分类器,并使域损失函数在其反射传播时的梯度与标签损失的梯度相反,从而保证域分类器不能区分样本来自于哪个域;最后引入了辅助分类任务,扩充了样本的同时使网络更具泛化能力。试验结果表明,本文方法优于主流的无监督域适应方法,在小样本遥感影像无监督分类中得到了较好的效果。

针对因样本量小而导致的遥感图像场景分类精度不高的问题,结合非下采样Contourlet变换（NSCT）、深度卷积神经网络（DCNN）和多核支持向量机（MKSVM）,提出了一种基于多尺度深度卷积神经网络（MS-DCNN）的遥感图像场景分类方法。首先利用非下采样Contourlet变换方法对遥感图像多尺度分解,然后对分解后的高频子带和低频子带分别用DCNN训练得到了不同尺度的图像特征,最后采用MKSVM综合多尺度特征并实现遥感图像场景分类。对标准遥感图像分类数据集的试验结果表明,本算法能够结合低频和高频子带对不同类别场景的识别优势,对遥感图像场景取得较好的分类结果。

Automatic understanding of food images has various applications in different fields, such as food intake monitor and food calorie estimation. Thus,the research on food related tasks,such as food image retrieval and classification has been one of the hot research topics in the field of multimedia analysis and applications recently. Existing methods mainly extract the visual features from the whole food image for further food analysis. The extracted features are lacking in robustness because of the background interference from the images. In order to solve this problem, we propose a Faster R-CNN (Region-based Convolutional Neural Network) based food retrieval and classification method. For the solution, we first detect the food candidate regions using Faster R-CNN, and then adopt the CNN network to extract the visual features from the detected food regions. Such extracted features are more discriminative for reducing the background interference. Furthermore, we select the annotated food images from the Visual G