Semantic Segmentation-Based Adaptive Mining Algorithm for Ceramic Cultural Resource Data

Abstract

Ceramic culture as Chinese culture has a long history and is the Chinese people’s spiritual home. The ceramic culture resource base contains a large number of images, videos, and other resources, so the collection of image category of data mining, as well as the finishing processing, is very critical. Thousands of years of ceramic data accumulation and impatient information demand have created a new point of contradiction for ceramic cultural resources. Therefore, in order to address this issue, we carried out a research study based on the concept of big data mining of ceramic cultural resource data, which is based on data fusion and feature extraction methods. We also considered semantic segmentation processing methods, which are used for data information management, scheduling, identification, collection, statistics, and aggregation of heterogeneous ceramic cultural big data. Further, a fine mining method for ceramic culture big data based on semantic segmentation is also proposed. As a result, the distributed storage of information and detection capability of ceramic cultural resource data are improved. The experimental results reveal that the proposed method performed better than the earlier approaches.