Binary Partition Tree for Semantic Object Extraction and Image Segmentation
In this work, we demonstrate a systematic way to analyze a binary partition tree representation of natural images for the purposes of archiving and segmentation. Within the tree structure, these problems are transformed into locating prevalent tree branches. With a user interface these points can be found manually by browsing branches. However, tree visualization is difficult due to the high node density. A simpler version of the tree is desired which facilitates subsequent retrieval whilst maintaining as much semantic detail as possible. By studying the evolution of region statistics, our method highlights nodes which represent the boundary between salient detail and provide a set of tree levels from which simplifications and segmentations can be derived. A series of subjective tests are performed to demonstrate the effectiveness of using the simplified trees for object extraction. Segmentation results are compared to ground truths showing semantic content is maintained