Adaptive perceptual color-texture image segmentation

Junqing Chen*, Thrasyvoulos N Pappas, Aleksandra Mojsilović, Bernice E. Rogowitz

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

163 Scopus citations

Abstract

We propose a new approach for image segmentation that is based on low-level features for color and texture. It is aimed at segmentation of natural scenes, in which the color and texture of each segment does not typically exhibit uniform statistical characteristics. The proposed approach combines knowledge of human perception with an understanding of signal characteristics in order to segment natural scenes into perceptually/semantically uniform regions. The proposed approach is based on two types of spatially adaptive low-level features. The first describes the local color composition in terms of spatially adaptive dominant colors, and the second describes the spatial characteristics of the grayscale component of the texture. Together, they provide a simple and effective characterization of texture that the proposed algorithm uses to obtain robust and, at the same time, accurate and precise segmentations. The resulting segmentations convey semantic information that can be used for content-based retrieval. The performance of the proposed algorithms is demonstrated in the domain of photographic images, including low-resolution, degraded, and compressed images.

Original languageEnglish (US)
Pages (from-to)1524-1536
Number of pages13
JournalIEEE Transactions on Image Processing
Volume14
Issue number10
DOIs
StatePublished - Oct 1 2005

Keywords

  • Adaptive clustering algorithm (ACA)
  • Content-based image retrieval (CBIR)
  • Gabor transform
  • Human visual system (HVS) models
  • Local median energy
  • Optimal color composition distance (OCCD)
  • Steerable filter decomposition

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

Fingerprint Dive into the research topics of 'Adaptive perceptual color-texture image segmentation'. Together they form a unique fingerprint.

Cite this