| dc.description.abstract | With the continuous improvement of computer hardware technology, the technology of image recognition is also constantly improving. For computers, it is a very simple matter to distinguish the content of a picture as a dog or a cat. However, to achieve high-accuracy identification, there are many conditions required: GPUs with good computing power, up to tens of thousands of training data, time of training. Nowadays, with the increasing popularity of artificial intelligence technology, different industries need to use machine learning and deep learning to achieve the desired target. However, when there is a need to combine artificial intelligence in areas other than academic or relatively unpopular, the amount of data is insufficient. In addition, many industry-owned machines do not have GPU-assisted training with superior computing performance.
At present, the mainstream image recognition method with high precision is still CNN-based architecture. It requires good GPU computing power and a certain training time to be successfully trained. Although there are also traditional feature extraction methods combined with PCANet based on neural networks. However, there is still big space for improvement in this section. This paper will use a similar architecture to PCANet, but replace the PCA part with the nearest feature line embedding(NFL). The NFL features a very good accuracy when the amount of data is small, and uses a similar architecture to PCANet for image analysis. It is the core of this paper to deal with and use the NFL to extract the necessary features and to use the SVM method to classify the images.
According to the analysis results, NFLENet can obtain 5%~10% higher recognition accuracy than PCANet when the amount of data is small, about 500 pieces of data training, and the training time is greatly reduced because of the reduced amount of data. | en_US |