<!-- tree 0 -->
-<_>
**<!-- root node -->**
-<feature>
-<rects>
<_>2 7 16 4 -1.</_>
<_>2 9 16 2 2.</_>
</rects>
<tilted>0</tilted>
</feature>
<threshold>4.3272329494357109e-003</threshold>
<left_val>0.0383819006383419</left_val>
<right_node>1</right_node>
</_>
-<_>
**<!-- node 1 -->**
-<feature>
-<rects>
<_>8 4 3 14 -1.</_>
<_>8 11 3 7 2.</_>
</rects>
<tilted>0</tilted>
</feature>
<threshold>0.0130761601030827</threshold>
<left_val>0.8965256810188294</left_val>
<right_val>0.2629314064979553</right_val>
What are <root node>
and <node 1>
respectively?
I thougth that each weak classifier matches with one haar-like feature
but when I see this XML code, there are two nodes and I am confused.
end I don't get what right/left_node or right/left_val means also.
There are different ways you train the samples. One way is to have "nsplits" option to be less than 2, where only root nodes are generated, with ,each root node corresponding to a haar feature, but if "nsplits" is greater than 1 you have root node split into leaf nodes such that the root node + leaf nodes = nsplits value and having 1 haar feature associated with each "node" arranged in a regression decision tree form (CART).
To answer your question short, even here each classifier is a haar feature but are arranged in a desision tree form having more than 1 leaf. For this particular example the training was done with nsplits = 2.