文件名称：大规模基于内容的图像检索
文件大小：1012KB
文件格式：RAR
更新时间：2015-10-08 03:08:03
大规模 基于内容 图像检索 CBIR
大规模图像检索的代码，matlab与c++混合编程。总结了目前图像检索领域目前主要存在的方法。通过阅读该代码，可以对于经典的“词袋”模型（bow模型）有个具体的了解，但是该代码没有提供前序的特征提取，是直接从对提取好的特征向量聚类开始的，包括了k-means，分层k-means（HKM）聚类，倒排文件的建立和索引等，该代码还提供了局部敏感哈希（LSH）方法。最后，这份代码是下面这篇论文的作者提供的， Indexing in Large Scale Image Collections: Scaling Properties and Benchmark-This C++/Matlab package implements several algorithms used for large scale image search. The algorithms are implemented in C++, with an eye on large scale databases. It can handle millions of images and hundreds of millions of local features. It has MEX interfaces for Matlab, but can also be used (with possible future modifications) from Python and directly from C++. It can also be used for approximate nearest neighbor search, especially using the Kd-Trees or LSH implementations. The algorithms can be divided into two broad categories, depending on the approach taken for image search: 1. Bag of Words: ---------------- The images are represented by histograms of visual words. It includes algorithms for computing dictionaries: * K-Means. * Approximate K-Means (AKM). * Hierarchical K-Means (HKM). It also includes algorithms for fast search: * Inverted File Index. * Inverted File Index with Extra Information (for example for implementing Hamming Embedding).