Natural Scene Image Segmentation Based on Multi-Layer Feature Extraction
This work addresses segmentation of natural scenes, which is an incremental improvement for computer vision applications.
The paper tackles natural image segmentation by extracting multi-layer features and merging regions, achieving higher accuracy on the Berkeley Segmentation Dataset compared to recent state-of-the-art methods.
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.