Adaptive perceptual color-texture image segmentation

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 kno...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on image processing Jg. 14; H. 10; S. 1524 - 1536
Hauptverfasser: Junqing Chen, Pappas, T.N., Mojsilovic, A., Rogowitz, B.E.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.10.2005
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adaptive clustering algorithm (ACA)
Algorithms
Applied sciences
Artificial Intelligence
Clustering algorithms
Color
Colorimetry - methods
Content based retrieval
content-based image retrieval (CBIR)
Data mining
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Feedback
Gabor transform
Gray-scale
human visual system (HVS) models
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image processing
Image retrieval
Image segmentation
Image texture analysis
Imaging, Three-Dimensional - methods
Information Storage and Retrieval - methods
Information theory
Information, signal and communications theory
Layout
local median energy
Numerical Analysis, Computer-Assisted
optimal color composition distance (OCCD)
Pattern Recognition, Automated - methods
Robustness
Signal and communications theory
Signal processing
Signal Processing, Computer-Assisted
Signal representation. Spectral analysis
Signal, noise
steerable filter decomposition
Studies
Telecommunications and information theory
Visual Perception
Performance evaluation
Gabor transform
Adaptive clustering algorithm (ACA)
Automatic classification
Photographic image
Adaptive algorithm
Image processing
Semantic analysis
Gabor transformation
content-based image retrieval (CBIR)
optimal color composition distance (OCCD)
Image coding
Content analysis
Gabor filter
Image texture
Texture analysis
Image resolution
Image retrieval
steerable filter decomposition
Information retrieval
Image compression
Low resolution
local median energy
Signal classification
Information transmission
Subjective evaluation
Color image
Photography
Image quality
Image segmentation
Gray scale
Content-based retrieval
human visual system (HVS) models
ISSN:1057-7149, 1941-0042
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
content type line 23
ObjectType-Undefined-1
ObjectType-Feature-3
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2005.852204