Cluster Segmentation of Thermal Image Sequences Using kd-Tree Structure

This paper presents optimization methods for the K-means segmentation algorithm for a sequence of thermal images. Images of the sample response in the frequency domain to the thermal stimulation with a known spectrum were subjected to cluster segmentation, grouping pixels with similar frequency characteristics. Compared were all pixel characteristics in the function of the frame number and grouped using the minimal sum of deviations of the pixels from their segment mean for all the frames of the processed image sequence. A new initialization method for the K-means algorithm, using density information, was used. A K-means algorithm with a kd-tree structure C# implementation was tested for speed and accuracy. This algorithm divides the set of pixels to the subspaces in the hierarchy of a binary tree. This allows skipping the calculation of distances of pixels to some centroids and pruning a set of centroid clusters through the hierarchy tree. Results of the segmentation were compared with the K-means and FCM algorithm MATLAB implementations.