| dc.description.abstract | Features are an important part of machine learning. Features are often the
reduced-dimensional representation of input data, feature calculation, extraction, and
fusion directly affect the final result of the network. This thesis proposes a total of 3
feature learning methods, and all of them have achieved good results.
In the chapter four of this thesis, the method of video feature extraction is mainly
introduced. In this thesis, 3D pre-trained CNN model is used to extract features
from continuous video frames, and the feature data is input into the classifier with
subnetwork node, and good results are obtained. And the experimental results show
that the pre-trained model effectively reduces the training time and improves the
testing accuracy. 3D CNN can fully extract the temporal and spatial features of
objects. For the subsequent classification accuracy has played a good effect.
Chapter three and five are mainly focus on EEG-based signal emotion recognition.
EEG data is a one-dimensional signal, but because the human cerebral cortex has
different feedback areas for different emotions, the data of EEG data on different
electrode channels are spatially related. And different channels of data have a positive
impact on different emotional states. Therefore, this chapter improves the accuracy
of the test results by converting the one-dimensional data of the EEG into a two-dimensional matrix and adding weights to the corresponding electrodes to extract
spatial features and fuse them with frequency-domain features.
Since the EEG signal is continuous in time, the time-domain features also have
a great effect on the final sentiment classification result. Therefore, the fifth chapter
adds the extraction of time-domain features based on the fourth chapter. The final
accuracy of emotion recognition has been significantly improved. | en_US |
