我不使用Matlab。所以我不知道Matlab与索引结构有多少成本相关联。它似乎不是为这样的东西设计的。

R-Trees似乎有很大的不同。从 http://elki.dbs.ifi.lmu.de/wiki/Benchmarking来判断a>某些算法可以从拥有良好的索引结构中获益匪浅。在110250图像颜色直方图数据集上，该网页上的数字快5到7倍。

根据我的经验，R-Trees确实很难获得对。但只有你想要全力以赴。如果您有静态数据库，则可以轻松使用批量加载的 R-Tree。批量加载和查询都不是很难做到。一旦你想用复杂的拆分策略进行R * -Tree优化，重新插入，平衡，并且通过智能缓存有效地和在磁盘上完成所有这些操作，R-Trees就会变得混乱。但只要您在内存中运行并且不动态添加对象，STR批量加载的R树将会有很多帮助并且更容易实现。

你可能仍然会在已经有一个工作R-Tree的东西上做得更好。用上面提到的rtree模块或ELKI说SQLite。

please, any one tell me how we can implement the R-tree structure in matlab to speed the image retrieval system , I would like to inform you that my database space a feature vector of Color Histogram (Multidimensional ) and also I I have a distance vector for similarity measure...

thanks

I don't use Matlab. So I do not have any idea how much cost is associated in Matlab with index structures. It doesn't appear to be designed for such things.

R-Trees seem to make quite a difference. Judging from http://elki.dbs.ifi.lmu.de/wiki/Benchmarking some algorithms can benefit immensely from having a good index structure. The numbers on that web page are 5 to 7 times faster on a 110250 image color histogram data set.

From my experience, R-Trees can indeed be quite hard to get right. But only if you want to go the full way. If you have a static database, you can get easily away with a bulk loaded R-Tree. Neither the bulk loading nor the queries are very hard to do. R-Trees get messy once you want to do the R*-Tree optimizations with complex split strategies, reinsertions, balancing, and do all this efficiently and on-disk with smart caching. But as long as you are operating in-memory and do not dynamically add objects, a STR bulk-loaded R-tree will help a lot and be a lot easier to implement.

You might still be better off building on something that has already a working R-Tree. Say SQLite with the rtree module or ELKI mentioned above.