In order to narrow the semantic gap existing in content-based image retrieval （CBIR）,a novel retrieval technology called auto-extended multi query examples （AMQE） is proposed.It expands the single one query image ...In order to narrow the semantic gap existing in content-based image retrieval （CBIR）,a novel retrieval technology called auto-extended multi query examples （AMQE） is proposed.It expands the single one query image used in traditional image retrieval into multi query examples so as to include more image features related with semantics.Retrieving images for each of the multi query examples and integrating the retrieval results,more relevant images can be obtained.The property of the recall-precision curve of a general retrieval algorithm and the K-means clustering method are used to realize the expansion according to the distance of image features of the initially retrieved images.The experimental results demonstrate that the AMQE technology can greatly improve the recall and precision of the original algorithms.展开更多
基金The National High Technology Research and Develop-ment Program of China (863 Program) (No.2002AA413420).
文摘In order to narrow the semantic gap existing in content-based image retrieval （CBIR）,a novel retrieval technology called auto-extended multi query examples （AMQE） is proposed.It expands the single one query image used in traditional image retrieval into multi query examples so as to include more image features related with semantics.Retrieving images for each of the multi query examples and integrating the retrieval results,more relevant images can be obtained.The property of the recall-precision curve of a general retrieval algorithm and the K-means clustering method are used to realize the expansion according to the distance of image features of the initially retrieved images.The experimental results demonstrate that the AMQE technology can greatly improve the recall and precision of the original algorithms.
文摘SQL是一种被广泛应用于操作关系数据库的编程语言,很多用户(如数据分析人员和初级程序员等)由于缺少编程经验和SQL语法知识,导致在编写SQL查询程序时会碰到各种困难.当前,使用程序合成方法根据<输入-输出>样例表自动生成相应的SQL查询程序,吸引了越来越多人的关注.所提ISST (正反例归纳合成)方法,能够根据用户编辑的含有少量元组的<输入-输出>示例表自动合成满足用户期望的SQL查询程序. ISST方法包括5个主要阶段:构建SQL查询程序草图、扩展工作表数据、划分正反例集合、归纳谓词和验证排序.在PostgreSQL在线数据库上验证SQL查询程序,并依据奥卡姆剃刀原则对已合成的SQL查询程序候选集打分排序.使用Java语言实现了ISST方法,并在包含28条样例的测试集上进行验证, ISST方法能正确合成其中的24条测试样例,平均耗时2 s.
