Adaptive perceptual color-texture image segmentation

Junqing Chen; Thrasyvoulos N. Pappas; Aleksandra Mojsilović; Bernice E. Rogowitz

doi:10.1109/TIP.2005.852204

Adaptive perceptual color-texture image segmentation

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

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

182 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 language	English (US)
Pages (from-to)	1524-1536
Number of pages	13
Journal	IEEE Transactions on Image Processing
Volume	14
Issue number	10
DOIs	https://doi.org/10.1109/TIP.2005.852204
State	Published - Oct 2005

Funding

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.

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

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10.1109/TIP.2005.852204

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title = "Adaptive perceptual color-texture image segmentation",

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.",

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",

author = "Junqing Chen and Pappas, {Thrasyvoulos N.} and Aleksandra Mojsilovi{\'c} and Rogowitz, {Bernice E.}",

note = "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.",

year = "2005",

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doi = "10.1109/TIP.2005.852204",

language = "English (US)",

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pages = "1524--1536",

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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

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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.

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KW - Content-based image retrieval (CBIR)

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KW - Human visual system (HVS) models

KW - Local median energy

KW - Optimal color composition distance (OCCD)

KW - Steerable filter decomposition

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Adaptive perceptual color-texture image segmentation

Abstract

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Keywords

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