Image feature extraction and reconstruction using principal component analysis algorithm in animation character design

The field of animation character design faces the problem of how to extract complex and diverse image features and achieve accurate reconstruction. To solve this problem, this study introduces a method that combines the PCA algorithm with a convolutional neural network (CNN). This paper uses PCA to preprocess the cartoon character image data. By calculating the covariance matrix, eigenvalue decomposition and other steps, the principal components of the main features are extracted, redundant information is removed, and key image features are retained. The reduced feature data is then input into the CNN model. The convolution layer of the model slides the convolution kernel on the image to extract local features at different levels, while the pooling layer downsamples the feature map to reduce the amount of data while retaining important features. The fully connected layer integrates all features to achieve deep learning and classification of the features of anime characters. Finally, based on the CNN model, the features of the anime characters are reconstructed to generate new anime characters with similar features. Compared with traditional LDA, the PSNR value after PCA-CNN reconstruction is improved from 10.5dB-19.8dB to 30.1dB-49.5dB, the SSIM is generally above 0.8, and the feature point matching rate is as high as 99.8%, which provides technical support for animation character design and promotes the further development of the animation industry.