Multiple pornographic image classification method based on image segmentation algorithm and deep learning

Abstract

A multiple pornographic image classification method based on an image segmentation algorithm and deep learning relates to the technical field of information, especially to the technical field of image identification. The method is characterized by comprising the following four major steps: skin color identification, principal component analysis of the skin color region, deep learning, and pornographic image identification based on a convolutional neural network. First, non-pornographic images are screened out through a skin color pixel detection and skin region partitioning algorithm based on the YCbCr theory. Undetermined images are input to a LeNet5-based convolutional neural network model for identification after feature extraction. Compared with the traditional identification based on skin color and features, the method can eliminate the noise influence of non-body-part images, is not constrained by light and human postures, and can greatly improve the accuracy of traditional pornographic image classification. Compared with general deep learning based on a convolutional neural network, the method does not need massive labeled images, and the characteristics of a deep residual network determine that the model can better analyze the characteristics of pornographic images. Only through about ten hours of training, an identification effect above 90% can be achieved.

Description

technical field [0001] The present invention relates to the field of information technology, especially the technical field of image processing. Background technique [0002] At present, most bad image recognition methods can be roughly divided into three categories according to their recognition objects: traditional skin color detection, feature detection and emerging pornographic image recognition based on neural networks. [0003] The traditional pornographic image recognition algorithm, which calculates the proportion of skin color in the image to calculate the degree of nudity, is limited by its theoretical basis, the diversity of the image environment and light, and the diversity of races make it difficult to establish a perfect Skin color model and improvement of detection accuracy, application number 200410042877.3 "a pornographic image detection method", which uses the ratio of the area of ​​the face area to the skin color area as the basis for judging pornographic ...

AI Technical Summary

Problems solved by technology

[0025] The first point is that the traditional pornographic image recognition algorithm calculates the proportion of skin color in the image to calculate the degree of nudity, which is limited by its theoretical basis, the diversity of the image environment and light, and the diversity of races. It is difficult to establish a perfect skin color model and improve the accuracy of detection;

[0026] The second point is that pornographic image recognition algorithms based on deep learning require a large number of image samples, which cannot adapt to the characteristics of diversification and differentiation of image content in network image formats. Greatly reduces the generalization ability and robustness of the system

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