TY - JOUR
T1 - Adaptive perceptual color-texture image segmentation
AU - Chen, Junqing
AU - Pappas, Thrasyvoulos N.
AU - Mojsilović, Aleksandra
AU - Rogowitz, Bernice E.
N1 - Funding Information:
Manuscript received August 7, 2003; revised August 9, 2004. This work was supported by the National Science Foundation (NSF) under Grant CCR-0209006. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Michel Schmitt.
PY - 2005/10
Y1 - 2005/10
N2 - 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.
AB - 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.
KW - Adaptive clustering algorithm (ACA)
KW - Content-based image retrieval (CBIR)
KW - Gabor transform
KW - Human visual system (HVS) models
KW - Local median energy
KW - Optimal color composition distance (OCCD)
KW - Steerable filter decomposition
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U2 - 10.1109/TIP.2005.852204
DO - 10.1109/TIP.2005.852204
M3 - Article
C2 - 16238058
AN - SCOPUS:27744584967
VL - 14
SP - 1524
EP - 1536
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
SN - 1057-7149
IS - 10
ER -