Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval

TLDR

Binary partition trees offer a compact, structured, multiscale representation of image regions with translation‑invariant connectivity. The paper investigates binary partition trees as a region‑oriented image representation, including computing and storing them for reuse in multiple applications. The tree representation supports efficient filtering, segmentation, information retrieval, visual browsing, and can be encoded with a moderate number of bits.

Abstract

This paper discusses the interest of binary partition trees as a region-oriented image representation. Binary partition trees concentrate in a compact and structured representation a set of meaningful regions that can be extracted from an image. They offer a multiscale representation of the image and define a translation invariant 2-connectivity rule among regions. As shown in this paper, this representation can be used for a large number of processing goals such as filtering, segmentation, information retrieval and visual browsing. Furthermore, the processing of the tree representation leads to very efficient algorithms. Finally, for some applications, it may be interesting to compute the binary partition tree once and to store it for subsequent use for various applications. In this context, the paper shows that the amount of bits necessary to encode a binary partition tree remains moderate.