Algorithm 3 Incremental clustering tree building
Input: p: feature point; K: branching factor
Output: T′: Incremental clustering tree
1:	select one point from Ci as central point randomly, compose the central point set {t1, t2, ,⋯ tK}
2:	$\mathit{min}\_\mathit{\text{length}}=\widetilde{t_1{t}_2}$ ⊲ min_length: minimum cluster distance
3:	for i = 1 → K − 1 do
4:	for j = i + 1 → K do
5:	if $\widetilde{t_i{t}_j}<\mathit{min}\_\mathit{\text{length}}$ then
6:	$\mathit{min}\_\mathit{\text{length}}=\widetilde{t_i{t}_j}$;
7:	end if
8:	end for
9:	end for
10:	$\mathit{min}\_\mathit{\text{length}}1=\widetilde{\text{p}{t}_1}$, k = 1, count = 0 ⊲ min_length1: minimum distance between p and clusters
11:	for i = 2 − K do
12:	if $\widetilde{\text{p}{t}_i}<\mathit{min}\_\mathit{\text{length}}1$ then
13:	$\mathit{min}\_\mathit{\text{length}}=\widetilde{\text{p}{t}_i}$, k = i
14:	end if
15:	if $\widetilde{\text{p}{t}_i}\le \mathit{min}\_\mathit{\text{length}}$ then
16:	count + +, D (count) = i ⊲ the number of clusters less thanmin_length and number of cluster
17:	end if
18:	end for
19:	if min_length1 > min_length then ⊲ create a new cluster
20:	CK+1 = {p}, K = K + 1
21:	else if count == 1 then
22:	Ck = Ck ∪ {p} ⊲ add to the k – th cluster
23:	else
24:	for i = 1 → count do ⊲ combine clusters
25:	Ck = Ck ∪ CD(i), K = K − 1
26:	end for
27:	Ck = Ck ∪ {p} ⊲ add to the merged cluster
28:	end if