Research on the algorithm of painting image style feature extraction based on intelligent vision

Abstract

Because the traditional image feature extraction algorithm does not smooth the image, the success rate of feature extraction is low, the average running time and the false positive rate are increased. In view of the above problems, this paper proposes an algorithm of painting image style feature extraction based on intelligent vision. According to the internal structure of the content image and the painting image, the similarity analysis and the smooth transfer of pixels are carried out, and then the painting image is smoothed with the semi-supervised learning method. On this basis, the similarity rule of painting image style is established, and all the style features are quantified, so as to obtain the self-similarity descriptor of painting image style. Then the similarity coefficient between the painting image and other sample images is calculated, and the similarity matrix is constructed, and the intelligent vision technology is used to complete the extraction of the painting image style features. Experimental results show that this algorithm can effectively reduce the average running time and false positive rate of painting image style feature extraction, and also improve the success rate of feature extraction.