逆向学习耦合多属性查询的图像排序/检索优化算法研究

Image Synchronization Sorting-retrieval Optimization Algorithm Based on Reverse Learning and Multi-attribute Queries

中文关键词: 多属性查询  图像排序-检索  逆向学习  属性相关性  检索精度

英文关键词: multi-attribute queries  image sorting-retrieval  reverse learning  attribute relevance  retrieval accuracy

中文摘要:

      目的 提出逆向学习耦合多属性查询的图像同步排序/检索优化算法， 以解决当前算法检索效率与精度不高等问题。方法 引入逆向学习概念， 利用复杂无损函数， 设计图像检索机制， 优化训练误差。考虑查询项属性相关性， 将训练图像分割成多个子集， 联合权重因子， 构造图像排序模型。对于给定的多属性查询， 文中算法可以利用查询项中隐含的单词属性完成检索。结果 文中算法支持多标记查询， 与当前图像排序搜索机制相比， 在多属性查询条件下， 文中算法具有更高的检索精度 （当查全率为80%时，精度较对照组分别提高了8.3%和13.2%） 与效率。结论 文中算法能够支持多属性查询， 能进一步增加检索精度。

英文摘要:

      Image synchronization sorting-retrieval optimization algorithm was proposed to solve the problems of low retrieval efficiency and accuracy of the existing algorithm. The image retrieval model was designed by introducing the concept of reverse learning. The complex lossless function was used to design the image retrieval mechanism and optimize the training error. Taking into account the attribute relevance of the query terms, we divided the training images into multiple subsets, and constructed the image ranking model by coupling the weighted factors to improve the effectiveness of attribute based image search. For given multi-attribute queries, the algorithm proposed in this paper could complete the retrieval using the word attribute contained in the queries. This algorithm supported multi-label queries, and had higher retrieval accuracy (when the recall rate was 80%, the accuracy was increased by 8.3% and 13.2% respectively as compared to the control group) and efficiency for multi-attribute queries as compared to the existing image sorting-retrieval mechanisms. In conclusion, the algorithm proposed could support multi-attribute queries and further increase the retrieval accuracy.