Affiliations: [a]
Jiangxi Institute of Science and Technology, Nanchang, Jiangxi, China
| [b]
Guangzhou Sport University, Guangzhou, Guangdong, China
Correspondence:
[*]
Corresponding author. Liu Fengying, Guangzhou Sport University, Guangzhou, Guangdong 510500, China. E-mail: liufengying00320@163.com.
Abstract: The image content retrieval can effectively promote the development of the entire industry. At present, sports competition is becoming more and more fierce, and the requirements for image content retrieval are getting higher and higher. In this paper, research has been carried out on image descriptor generation, image feature quantization and coding, accurate nearest neighbor cluster center fast search, multi-dimensional inverted index construction and fast retrieval. Moreover, based on deep learning, this paper constructed an effective detection algorithm for the characteristics of sports images, and compared the image shape and color as examples. It can be seen from the comparative study that the research method of this paper can effectively reduce the size of the candidate set of query results without affecting the accuracy of the query, which is of great significance for improving the speed of image query and has certain significance for promoting the development of sports public industry.
