Research on image screening model of ancient villages

Abstract

Ancient villages are the carrier of a nation's profound history and culture. They are the integration of history, culture, architecture and sculpture, and have many value attributes. With the development of society, the protection of ancient villages is very important. The establishment of digital archives is an important means to protect ancient villages. Because of the large number and wide distribution of ancient villages, crowd sourcing can quickly and widely access the digital resources of ancient villages. Because of the uneven quality and repetition of the images collected from ancient villages, it is necessary to screen the images of ancient villages. Therefore, this paper proposes a screening model of ancient villages based on SIFT and convolution neural network. Firstly, this paper chooses edge gray change rate and NIQE quality score to evaluate the quality of ancient village image; secondly, extracts SIFT features of image for matching, calculates matching similarity to determine whether the matched image is myopic repetition. Finally, the image is filtered or preserved by using convolution neural network with the edge gray change rate, NIQE quality score and some image attributes as features. Experiments show that the ancient village image screening model designed in this paper has higher accuracy and recall rate than other methods, and has better screening effect.