Abstract:

The feature extraction method of cosmetic packaging symbol elements based on big data clustering was proposed. The level set function was initialized to obtain the local standard deviation image of cosmetic packaging symbol element image. The evolution direction of local standard deviation image pixels on contour curve was determined according to the cosine similarity. The level set evolution improved the SPF function to complete the symbol element segmentation. The discrete sample spectrum characteristics of big data were calculated, and the vector matrix of the particle optimal solution of the data clustering center was obtained by the confidence degree to complete the data clustering. By combining convolution neural network and AutoEncoder, the maximum activation value was obtained in the nodes existing in the output layer through convolution, filtering and pooling operations, and the feature extraction of cosmetic packaging symbol elements was realized. The experimental results show that the proposed method has shorter feature extraction time, higher feature recognition ability and higher extraction accuracy.

Key words: cosmetic packaging, big data clustering, symbol element, feature extraction