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.

中文翻译：

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基于深度学习的图像内容特征检索在体育公共产业中的应用

图像内容检索可以有效地促进整个行业的发展。当前，体育比赛越来越激烈，对图像内容检索的要求也越来越高。本文对图像描述符的生成，图像特征的量化和编码，精确的最近邻簇中心快速搜索，多维倒排索引的构建和快速检索等方面进行了研究。此外，在深度学习的基础上，针对运动图像的特征构造了一种有效的检测算法，并以图像的形状和颜色为例进行了比较。从比较研究中可以看出，本文的研究方法可以有效地减小查询结果候选集的大小，而不会影响查询的准确性，