Name
Title | ||
|---|---|---|
An unsupervised image segmentation method combining graph clustering and high-level feature representation |
Abstract | ||
|---|---|---|
Image segmentation is one of the most important assignments in computer vision. In this paper, we present an unsupervised segmentation method that combines graph-based clustering and high-level semantic features. We over-segment the given image into a collection of superpixels. Various low-level features assemble a descriptor of each superpixel. Besides the intrinsic image features such as color, texture and gradient, we add image saliency into the low-level visual features as prior knowledge of human perception. Instead of using the low-level features directly, we design a graph-based method to segment the image by clustering the high-level semantic features learned from a neural network. We test the proposed method on two well-known datasets. The experimental evaluation validates that our approach can provide consistent and meaningful segmentation. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.neucom.2020.05.073 | Neurocomputing |
Keywords | DocType | Volume |
Segmentation,Unsupervised,Autoencoder architecture,Clustering | Journal | 409 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xue Jiao | 1 | 1 | 0.69 |
| Yonggang Chen | 2 | 267 | 20.44 |
| Rui Dong | 3 | 0 | 0.34 |