Connecting image similarity retrieval with consistent labeling problem by introducing a match-all label
Connecting image similarity retrieval with consistent labeling problem by introducing a match-all label
复制标题
通过引入全匹配标签将图像相似性检索与一致标签问题联系起来
DOI：
10.1109/fuzz.2001.1008916
复制
发表时间：
2001
期刊：
影响因子：
--
通讯作者：
K. Toraichi
中科院分区：
文献类型：
--
作者：
P. Kwan;K. Kameyama;K. Toraichi
作者以前的工作在图像相似性检索松弛标记过程（2001年）需要添加局部约束，以获得一个初始的一组兼容的对象和标签对图像，以确保标签的一致性收敛。这种方法存在过度指定约束的问题，导致潜在的宝贵信息被过早删除。为了解决这个问题，我们引入了Match-all标签的概念，用于那些没有满足这些约束的对象。它的作用是给它们一个定义的标记概率，并允许它们通过兼容性模型参与全局对应。我们证明了这种增强的公式仍然满足Hummel和Zucker（1983）中关于标记一致性的定理的条件。在收敛时，对象的集合和它们最一致的标签构成图像对的最佳部分标签。
The authors' previous work in image similarity retrieval by relaxation labeling processes (2001) required adding local constraints to obtain an initial set of compatible objects and labels on pairs of images to ensure labeling consistency at convergence. This approach suffers from the problem of over-specified constraints that leads to potentially invaluable information being prematurely removed. To address this problem, we introduce the idea of a Match-all label for objects that failed these constraints. It serves to give them a defined labeling probability as well as allowing them participate in global correspondences through the compatibility model. We show that this enhanced formulation still meets the conditions for a theorem on labeling consistency in Hummel and Zucker (1983) to be satisfied. At convergence, the set of objects and their most consistent labels constitute a best partial labeling for the pair of images.