Direct Estimation of Appearance Models for Segmentation

TL;DR

This paper introduces two novel algorithms for directly estimating appearance models from images, enabling more effective segmentation without explicit pixel-region association, based on algebraic relations between local image statistics and region appearance.

Contribution

The paper presents a new approach to estimate appearance models directly from images using algebraic expressions, including a least squares and a spectral method, improving segmentation techniques.

Findings

Algorithms effectively estimate appearance models from images.

Methods improve segmentation accuracy and efficiency.

Experimental results validate practical effectiveness.

Abstract

Image segmentation algorithms often depend on appearance models that characterize the distribution of pixel values in different image regions. We describe a new approach for estimating appearance models directly from an image, without explicit consideration of the pixels that make up each region. Our approach is based on novel algebraic expressions that relate local image statistics to the appearance of spatially coherent regions. We describe two algorithms that can use the aforementioned algebraic expressions to estimate appearance models directly from an image. The first algorithm solves a system of linear and quadratic equations using a least squares formulation. The second algorithm is a spectral method based on an eigenvector computation. We present experimental results that demonstrate the proposed methods work well in practice and lead to effective image segmentation algorithms.