FEATURE EXTRACTION in Chinese translation

• 特征提取
• 特征抽取

Examples of using Feature extraction in English and their translations into Chinese

Face Image Preprocessing: Image preprocessing for faces is based on face detection results, processing the images and ultimately serving the feature extraction process.
人脸图像预处理：对于人脸的图像预处理是基于人脸检测结果，对图像进行处理并最终服务于特征提取的过程。

Given that deep learning systems are good at feature extraction, how can you reuse existing networks to perform feature extraction for other tasks?
鉴于深度学习系统擅长特征提取，我们该如何重用现有网络以执行其他任务的特征提取呢?

Given that deep learning systems are good at feature extraction, how can you reuse existing networks to perform feature extraction for other tasks?
考虑到深度学习系统强大的特征提取能力，如何重复使用现有网络来执行其他任务的特征提取？?

Performing feature extraction over the image beforeproposing regions, thus only running one CNN over the entire image instead of 2000 CNN's over 2000 overlapping regions.
在提出区域之前在图像上执行特征提取，因此在整个图像上仅运行一个CNN而不是在超过2000个重叠区域运行2000个CNN。

Feature extraction: characteristics extracted from the images.
特征提取：从图像中提取各种复杂度的特征。

The pre-processing stage is sometimes also called feature extraction.
该预处理阶段有时也称为特征提取。

This pre-processing stage is sometimes also called feature extraction.
该预处理阶段有时也称为特征提取。

Feature extraction is automatic(without human intervention) and multi-layered.
特征提取是自动（没有人为干预）而且多层的。

Face feature extraction is performed on certain features of the face.
面部特征提取是对面部的某些特征进行的。

This pre-processing stage is sometimes also called feature extraction.
这样的预处理我们有时也把它称为特征抽取（featureextraction）。

Feature extraction is a concept in computer vision and image processing.
特征检测是计算机视觉和图像处理中的一个概念。

Python provides an excellent environment for performing basic text processing and feature extraction.
扩展到大型数据集Python提供了一个良好的环境进行基本的文本处理和特征提取。

The Scikit-learn preprocessing tools are important in feature extraction and normalization during data analysis.
Scikit-learn预处理工具在数据分析期间的特征提取和规范化中非常重要。

For audio preprocessing, aDSP and its Elite framework is suitable to do feature extraction in real-time.
对于音频预处理，aDSP及其Elite框架适用于实时特征提取。

CNNs eliminate the need for manual feature extraction- the features are learned directly by the CNN.
CNN消除了手动提取特征的需要-CNN直接学习特征。

The process can be summarized as three parts: fingerprint acquisition, fingerprint feature extraction and fingerprint matching.
该过程可归纳为三个部分：指纹采集，指纹特征提取和指纹匹配。

Feature extraction places a huge burden on the programmer especially in complex problems, such as object recognition.
特征提取给程序员带来了巨大负担，尤其是在复杂问题中，比如物体识别。

The LeNet CNN architecture is made up of several layers that implement feature extraction, and then classification.
LeNetCNN架构由若干层组成，这些层实现特征提取，然后进行分类。

To improve our accuracy, we incorporated an automatic feature extraction module into our model, depicted below.
为了提高我们的准确度，我们在模型中引入了一个自动特征提取模块，如下所示：.

Also, there are techniques that replace the classical feature extraction methods with more complex and efficient neural-based methods.
同时，还有能够用更加复杂高效的基于神经模型的方法取代经典特征提取方法的技术。