Different home textile patterns have different emotional expressions. Emotion evaluation of home textile patterns can effectively improve the retrieval performance of home textile patterns based on semantics. It can not only help designers make full use of existing designs and stimulate creative inspiration but also help users select designs and products that are more in line with their needs. In this study, we develop a three-stage framework for home textile pattern emotion labeling based on artificial intelligence. To be specific, first of all, three kinds of aesthetic features, i.e., shape, texture, and salient region, are extracted from the original home textile patterns. Then, a CNN (convolutional neural network)-based deep feature extractor is constructed to extract deep features from the aesthetic features acquired in the previous stage. Finally, a novel multi-view classifier is designed to label home textile patterns that can automatically learn the weight of each view. The three-stage framework is evaluated by our data and the experimental results show its promising performance in home textile patterns labeling.
Deep Feature Learning for Disease Risk Assessment Based on Convolutional Neural Network With Intra-Layer Recurrent Connection by Using Hospital Big Data
