Natural Scene Image Segmentation Based on Multi-Layer Feature Extraction

TL;DR

This paper proposes a multi-layer feature extraction method for natural image segmentation, utilizing color, gradient, and statistical properties, and demonstrates improved accuracy over recent techniques on benchmark datasets.

Contribution

The paper introduces a novel multi-layer feature extraction approach combined with GMM and region merging functions for improved natural image segmentation.

Findings

Achieves higher accuracy than recent methods on Berkeley Segmentation Dataset.

Effective use of multi-layer features improves segmentation quality.

Region merging functions enhance the coherence of segmented regions.

Abstract

This paper addresses the problem of natural image segmentation by extracting information from a multi-layer array which is constructed based on color, gradient, and statistical properties of the local neighborhoods in an image. A Gaussian Mixture Model (GMM) is used to improve the effectiveness of local spectral histogram features. Grouping these features leads to forming a rough initial over-segmented layer which contains coherent regions of pixels. The regions are merged by using two proposed functions for calculating the distance between two neighboring regions and making decisions about their merging. Extensive experiments are performed on the Berkeley Segmentation Dataset to evaluate the performance of our proposed method and compare the results with the recent state-of-the-art methods. The experimental results indicate that our method achieves higher level of accuracy for natural images compared to recent methods.