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...

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Bibliographic Details
Published in:	IEEE transactions on image processing Vol. 14; no. 10; pp. 1524 - 1536
Main Authors:	Junqing Chen, Pappas, T.N., Mojsilovic, A., Rogowitz, B.E.
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.10.2005 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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 Access:	Get full text
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Summary:	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.
Bibliography:	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