Reversible Image Merging for Low-level Machine Vision
This work addresses low-level machine vision tasks like image segmentation, but it appears incremental as it builds on hierarchical clustering methods without clear broad impact.
The paper tackles the problem of hierarchical pixel clustering and image segmentation by introducing a reversible image merging model, resulting in a method to generate optimized piecewise constant approximations with convex sequences of total squared errors.
In this paper a hierarchical model for pixel clustering and image segmentation is developed. In the model an image is hierarchically structured. The original image is treated as a set of nested images, which are capable to reversibly merge with each other. An object is defined as a structural element of an image, so that, an image is regarded as a maximal object. The simulating of none-hierarchical optimal pixel clustering by hierarchical clustering is studied. To generate a hierarchy of optimized piecewise constant image approximations, estimated by the standard deviation of approximation from the image, the conversion of any hierarchy of approximations into the hierarchy described in relation to the number of intensity levels by convex sequence of total squared errors is proposed.