问题
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
回答1:
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.
回答2:
Implementing R-tree is not really a simple task. You can use matlab binding for the LidarK library, it should be fast enough. The code is here: http://graphics.cs.msu.ru/en/science/research/3dpoint/lidark
If you decide to use kd-tree (which is typical for image retrieval), there's a good implementation too. http://www.cs.ubc.ca/~mariusm/index.php/FLANN/FLANN
回答3:
I'm not familiar with R-trees specifically but in general trees are dynamic data structures. Matlab doesn't really do dynamic data structures unless you start using its OO facilities. If you don't want to do that you can flatten your tree into a cell array. For example I'll write a (strictly) binary tree flattened into a cell array, which will save me having to draw a tree. Here goes:
{1,{2},{3}}
which represents a binary tree with root 1 and branches left to 2, right to 3. I can make this deeper:
{1,{2,{5,6}},{3,{7,8}}}
which adds another level to the previous tree. If you want to add data at any of the nodes, then your (first) tree might look like this:
{1,[a b c],{2,[e f]},{3,[h i j k l]}}
An alternative to this would be to define your nodes separately, like this
node1 = [a b c]; node2 = [e f]; node3 = [h i j k l],
then your tree becomes
{node1, node2, node3}
Your problem then becomes writing functions to build and to traverse the tree in your chosen representation. Most tree functions are best written as recursions. Any good text, and lots of Internet sites, will tell you all that you want to know about such functions.
来源：https://stackoverflow.com/questions/2086294/r-tree-implementation-in-matlab