Density-based clustering of image keypoints

I have implemented the DBSCAN algorithm for clustering image keypoints, I'm using C++ and OpenCV, I have been following the pseudocode on the wiki page pretty strictly and its working but I get the feeling its a very naive implementation and could be improved in terms of performance, so I was hoping you could offer me some feedback on how to improve it, thanks.

/* DBSCAN - density-based spatial clustering of applications with noise */

vector<vector<KeyPoint>> DBSCAN_keypoints(vector<KeyPoint> *keypoints, float eps, int minPts)
{
vector<vector<KeyPoint>> clusters;
vector<bool> clustered;
vector<int> noise;
vector<bool> visited;
vector<int> neighborPts;
vector<int> neighborPts_;
int c;

int noKeys = keypoints->size();

//init clustered and visited
for(int k = 0; k < noKeys; k++)
{
    clustered.push_back(false);
    visited.push_back(false);
}

//C =0;
c = 0;
clusters.push_back(vector<KeyPoint>()); //will stay empty?

//for each unvisted point P in dataset keypoints
for(int i = 0; i < noKeys; i++)
{
    if(!visited[i])
    {
        //Mark P as visited
        visited[i] = true;
        neighborPts = regionQuery(keypoints,&keypoints->at(i),eps);
        if(neighborPts.size() < minPts)
            //Mark P as Noise
            noise.push_back(i);
        else
        {
            clusters.push_back(vector<KeyPoint>());
            c++;
            //expand cluster
            // add P to cluster c
            clusters[c].push_back(keypoints->at(i));
            //for each point P' in neighborPts
            for(int j = 0; j < neighborPts.size(); j++)
            {
                //if P' is not visited
                if(!visited[neighborPts[j]])
                {
                    //Mark P' as visited
                    visited[neighborPts[j]] = true;
                    neighborPts_ = regionQuery(keypoints,&keypoints->at(neighborPts[j]),eps);
                    if(neighborPts_.size() >= minPts)
                    {
                        neighborPts.insert(neighborPts.end(),neighborPts_.begin(),neighborPts_.end());
                    }
                }
                // if P' is not yet a member of any cluster
                // add P' to cluster c
                if(!clustered[neighborPts[j]])
                    clusters[c].push_back(keypoints->at(neighborPts[j]));
            }
        }

    }
}
return clusters;
}

vector<int> regionQuery(vector<KeyPoint> *keypoints, KeyPoint *keypoint, float eps)
{
float dist;
vector<int> retKeys;
for(int i = 0; i< keypoints->size(); i++)
{
    dist = sqrt(pow((keypoint->pt.x - keypoints->at(i).pt.x),2)+pow((keypoint->pt.y - keypoints->at(i).pt.y),2));
    if(dist <= eps && dist != 0.0f)
    {
        retKeys.push_back(i);
    }
}
return retKeys;
}