A hierarchical image segmentation algorithm

This paper presents a hierarchical image representation that preserves spatial relationships, and an image segmentation algorithm that efficiently constructs this representation. The overall structure of the hierarchical representation assumes a tree-like organization, in which a node at a higher level represents of a group of nodes from the next lower level. The root node of the representation is the full image, and the leaf nodes are K x K blocks of pixels (usually 4 x 4 or 8 x 8), called blobs. The segmentation algorithm builds this hierarchical representation using an efficient clustering algorithm for grouping nodes progressively into larger objects. While performing the union of nodes at one level into a single parent node at the next higher level of the tree, nodes are grouped together according to similarities between their feature vectors, which include such features as color information, orientation, texture, size, energy, and neighbor information. The computation complexity of the hierarchical image segmentation algorithm is O(MN +k(2)/MNlog(k(2)/MN)), where M, N are the height and width of the original image. Experimental results show that this algorithm can segment images and extract objects effectively.