| dc.description.abstract | Recent years, artificial intelligence and deep learning are widely used in various fields. For example, vehicle identification, AlphaGo, AOI automatic optical detection, natural language processing, graphic recognition, etc. The number of people visiting the art exhibition at their free time has been increased in recent year,and the digit of paintings has become an emerging trend in traditional art galleries. So we provide an application that captures the features of the paintings and then sorts them by the similarity of the features so that users can obtain works in the database that are similar in style to their paintings.
The propose system uses the convolutional neural network-VGG16 architecture. The module training data is about 1.2 million images of ImageNet as training. In this paper, the paintings are used as test data, and the weights of the first layer of the full-connected layer is obtained as an independent feature vector retraining for each image. And in order to speed up the system operation, principal component analysis (PCA) is used for consistent dimensionality reduction.
Since everyone has a subjective awareness of the style of the paintings, our system designed a feedback application. Through the weights of the continuous training paintings, the system filters out the paintings that the user does not like and pushes the related paintings according to personal preference. | en_US |