Content-adaptive non-parametric texture similarity measure

Motaz Alfarraj; Yazeed Alaudah; Ghassan Alregib

doi:10.1109/MMSP.2016.7813338

Content-adaptive non-parametric texture similarity measure

Motaz Alfarraj^*, Yazeed Alaudah, Ghassan Alregib

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

18 Scopus citations

Abstract

In this paper, we introduce a non-parametric texture similarity measure based on the singular value decomposition of the curvelet coefficients followed by a content-based truncation of the singular values. This measure focuses on images with repeating structures and directional content such as those found in natural texture images. Such textural content is critical for image perception and its similarity plays a vital role in various computer vision applications. In this paper, we evaluate the effectiveness of the proposed measure using a retrieval experiment. The proposed measure outperforms the state-of-the-art texture similarity metrics on CUReT and PerTex texture databases, respectively.

Original language	English
Title of host publication	2016 IEEE 18th International Workshop on Multimedia Signal Processing, MMSP 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509037247
DOIs	https://doi.org/10.1109/MMSP.2016.7813338
State	Published - 10 Jan 2017
Externally published	Yes

Publication series

Name	2016 IEEE 18th International Workshop on Multimedia Signal Processing, MMSP 2016

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

Feature extraction
Image retrieval
Image similarity
Texture analysis

ASJC Scopus subject areas

Signal Processing
Media Technology

Access to Document

10.1109/MMSP.2016.7813338

Cite this

Alfarraj, M., Alaudah, Y., & Alregib, G. (2017). Content-adaptive non-parametric texture similarity measure. In 2016 IEEE 18th International Workshop on Multimedia Signal Processing, MMSP 2016 Article 7813338 (2016 IEEE 18th International Workshop on Multimedia Signal Processing, MMSP 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MMSP.2016.7813338

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keywords = "Feature extraction, Image retrieval, Image similarity, Texture analysis",

author = "Motaz Alfarraj and Yazeed Alaudah and Ghassan Alregib",

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doi = "10.1109/MMSP.2016.7813338",

language = "English",

series = "2016 IEEE 18th International Workshop on Multimedia Signal Processing, MMSP 2016",

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Alfarraj, M, Alaudah, Y & Alregib, G 2017, Content-adaptive non-parametric texture similarity measure. in 2016 IEEE 18th International Workshop on Multimedia Signal Processing, MMSP 2016., 7813338, 2016 IEEE 18th International Workshop on Multimedia Signal Processing, MMSP 2016, Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/MMSP.2016.7813338

Content-adaptive non-parametric texture similarity measure. / Alfarraj, Motaz; Alaudah, Yazeed; Alregib, Ghassan.
2016 IEEE 18th International Workshop on Multimedia Signal Processing, MMSP 2016. Institute of Electrical and Electronics Engineers Inc., 2017. 7813338 (2016 IEEE 18th International Workshop on Multimedia Signal Processing, MMSP 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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PY - 2017/1/10

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N2 - In this paper, we introduce a non-parametric texture similarity measure based on the singular value decomposition of the curvelet coefficients followed by a content-based truncation of the singular values. This measure focuses on images with repeating structures and directional content such as those found in natural texture images. Such textural content is critical for image perception and its similarity plays a vital role in various computer vision applications. In this paper, we evaluate the effectiveness of the proposed measure using a retrieval experiment. The proposed measure outperforms the state-of-the-art texture similarity metrics on CUReT and PerTex texture databases, respectively.

AB - In this paper, we introduce a non-parametric texture similarity measure based on the singular value decomposition of the curvelet coefficients followed by a content-based truncation of the singular values. This measure focuses on images with repeating structures and directional content such as those found in natural texture images. Such textural content is critical for image perception and its similarity plays a vital role in various computer vision applications. In this paper, we evaluate the effectiveness of the proposed measure using a retrieval experiment. The proposed measure outperforms the state-of-the-art texture similarity metrics on CUReT and PerTex texture databases, respectively.

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KW - Image retrieval

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M3 - Conference contribution

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BT - 2016 IEEE 18th International Workshop on Multimedia Signal Processing, MMSP 2016

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ER -

Content-adaptive non-parametric texture similarity measure

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this