Interactive image segmentation based on multi-layer random forest classifiers

Since fully automatic image segmentation methods often fail for most complex images, researchers turn to the interactive segmentation paradigm to achieve better segmentation performance. However, many interactive image segmentation algorithms are highly dependent on user interactive information. This paper presents a novel interactive image segmentation algorithm based on multi-layer random forests. Given a small amount of user input markers, region merging is done according to the merging rule, in which both the color histogram and gradient orientation histogram of the region are included to avoid the merging error. To speed up the calculation of gradient orientation histogram, breadth-first search is used to determine the intersection of two adjacent regions. Then, we relabel the training samples with k-means algorithm and Silhouette index and further perform the first layer random forest classification. Next, we reconstruct the training samples with the adjacent superpixel pairs and use the second layer random forest classifiers to classify the superpixels whose prediction confidence is lower than the threshold after the first layer random forest classification. Experiments on real natural images are conducted to demonstrate the performance of the proposed algorithm.