Unsupervised Image Classification Based on Fully Fuzzy Voronoi Tessellation
Source abstract
Abstract
High noise resistance and high boundary fitting accuracy have always been the goals of image classification. However, the two mutually constrain each other, making it extremely difficult to reach equilibrium. To deal with this problem, the unsupervised image classification algorithm based on fully fuzzy Voronoi tessellation is proposed. It extends Voronoi tessellation from hard to fuzzy, and proposes a hierarchical fuzzy membership model, i.e., pixels fuzzily belong to Voronoi polygons and polygons fuzzily belong to clusters. The objective function is established based on the hierarchical fuzzy membership model by fully considering the transitivity of fuzziness between different levels. Then, the optimal classification result can be obtained by the fuzzy comprehensive decision theory under the best parameter solution. The first level retains the flexibility of pixels while modeling spatial constraints. The second level determines which class the polygon belongs to under the constraint of the first level. It provides an effective way of balancing noise resistance and boundary fitting. In addition, the Voronoi tessellation is explicitly expressed in the objective function in the form of the mathematical model, which allows it to obtain the optimal value through analytical solutions instead of the previous random sampling method. It greatly increases the convergence speed of the algorithm. Experiments have been performed on simulated and several remote sensing images with seven comparing algorithms to demonstrate the effectiveness of the proposed algorithm.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Image and Object Detection Techniques · Medical Image Segmentation Techniques · Advanced Measurement and Detection Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Center for Geospatial Information, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Shenzhen Engineering Laboratory of Ocean Environmental Big Data Analysis and Application, Shenzhen 518055, China
School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China
Funding
National Key Research and Development Program of China · Undertaking National Science and Technology Major Project by Shenzhen Technology and Innovation Council · Scientific Research Project of Ecology Environment Bureau of Shenzhen Municipality: “Research on the Application of Multi-source Data in the Investigation and Evaluation of Ecosystem Status in Shenzhen” · National Natural Science Foundation of China · Excellent Youth Innovation Foundation of the Shenzhen Institutes of Advanced Technology (SIAT) of the Chinese Academy of Science (CAS)
References (34)
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