Abstract 我们提出了一种有效的谱方法来寻找两组特征之间的一致对应关系.我们建立了一个图的邻接矩阵M,它的节点代表了潜在的对应,而链接上的权重代表潜在的对应之间的成对协议.正确的分配可在彼此之间建立联系,从而形成一个强连接的集群.不正确的对应只与其他对应建立偶然的联系,所以它们不太可能属于强连接的簇.我们根据它们属于M主簇的强烈程度恢复正确的分配,通过利用M的主特征向量,并加强整体对应映射(一对一或一对多)所需要的映射约束.实验结果表明,我们的方法对离群值有鲁棒性,在匹配率方面准确,比现有…

转载自:Chris Choy's blog Universal Correspondence Network In this post, we will give a very high-level overview of the paper in layman’s terms. I’ve received some questions regarding what the Universal Correspondence Network (UCN) is and the limitations…

1. Clustering Analysis Clustering is the process of grouping a set of (unlabeled) data objects into multiple groups or clusters such that objects within a cluster have high similarity, but are very dissimilar to objects in other clusters. Dissimilari…

参考文章:An efficient and robust line segment matching approach based on LBD descriptor and pairwise geometric consistency ----Lilian Zhang  , Reinhard Koch 第三部分:Graph matching using spectral technique 介绍线检测和描述之后,本节我们介绍方法来构造两组LineVecs之间的关系图并且在图中建立匹配结果.在此…

一. 近邻搜索 从这里开始我将会对LSH进行一番长篇大论.因为这只是一篇博文,并不是论文.我觉得一篇好的博文是尽可能让人看懂,它对语言的要求并没有像论文那么严格,因此它可以有更强的表现力. 局部敏感哈希,英文locality-sensetive hashing,常简称为LSH.局部敏感哈希在部分中文文献中也会被称做位置敏感哈希.LSH是一种哈希算法,最早在1998年由Indyk在[1]上提出.不同于我们在数据结构教材中对哈希算法的认识,哈希最开始是为了减少冲突方便快速增删改查,在这里LSH恰恰相…

By X Wang Update History:Web Version latest update: 4/6/2014PDF Version latest update: 1/16/2014 The following are top 10 algorithms related topics for coding interviews. As understanding those concepts requires much more effort, this list below only…

The following are top 10 algorithms related concepts in coding interview. I will try to illustrate those concepts though some simple examples. As understanding those concepts requires much more efforts, this list only serves as an introduction. They…

Contents Tutorial Hello, World Command-Line Arguments Finding Duplicate Lines A Web Server Loose Ends Program Structure Names Declarations Variables Assignments Type Declarations Packages and Files Scope Basic Data Types Integers Floating-Point Numbe…

SVM is capable of performing linear or nonlinear classification,regression,and even outlier detection. SVMs are particularly well suited for classification of complex but small- or medium-sized datasets. Linear SVM Classification: Soft Margin Classif…

本文转载自:链接 Highlights Three different methods for parallel gradient boosting decision trees. My algorithm and implementation is competitve with (and in many cases better than) the implementation in OpenCV and XGBoost (A parallel GBDT library with 750+…