Texture image segmentation using spectral clustering

Hui Du, Yuping Wang*, Xiaopan Dong, Yiu Ming CHEUNG

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

3 Citations (Scopus)


Clustering is a popular and effective method for texture image segmentation. However, most cluster methods often suffer the following problems: need a huge space and a lot of computation when the input data is large. To save the space and computation, we construct a novel algorithm for image segmentation. It consists of two phases: Sampling and clustering. First, we put some detectors into the data space uniformly using orthogonal design method. These detectors can move and merge according to the law of universal gravitation. When the detectors are in a stable status (i.e., do not move), these detectors are used as the representative samples to the next step. Second, to further improve the efficiency and avoid dependence on parameters, the Self-tuning Spectral Clustering (SSC) is used to the representative samples to do the clustering. As a result, the proposed algorithm can quickly and precisely realize the clustering for texture image segmentation.

Original languageEnglish
Title of host publicationHCI International 2015 – Posters Extended Abstracts - International Conference, HCI International 2015, Proceedings
EditorsConstantine Stephanidis
PublisherSpringer Verlag
Number of pages6
ISBN (Print)9783319213798
Publication statusPublished - 2015
Event17th International Conference on Human Computer Interaction, HCI 2015 - Los Angeles, United States
Duration: 2 Aug 20157 Aug 2015

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929


Conference17th International Conference on Human Computer Interaction, HCI 2015
Country/TerritoryUnited States
CityLos Angeles

Scopus Subject Areas

  • Computer Science(all)
  • Mathematics(all)

User-Defined Keywords

  • Feature extraction
  • Gray level histogram
  • Sampling
  • Spectral clustering
  • Texture image segmentation


Dive into the research topics of 'Texture image segmentation using spectral clustering'. Together they form a unique fingerprint.

Cite this