Research on Folk Handicraft Image Recognition Based on Neural Networks and Visual Saliency

Abstract

How to identify quickly the images of folk arts and crafts works has become a difficult problem of cultural heritage value mining. Therefore, combined with the image recognition technology can improve the accuracy of the identification of folk arts and crafts works. This paper improves the ITTI significant model based on the method of linear addition of significant maps with the same proportion. Firstly, Bayesian model and Gaussian model are used to extract the probability distribution of image feature vector; secondly, the k-means algorithm is used to identify image accuracy extraction work, and finally ALOI database is used to test judgment image recognition accuracy; experimental results found that the improved technology does help to improve the folk arts and handicraft image recognition accuracy.