Optimized image resizing using flow-guided seam carving and an interactive genetic algorithm

Jong Chul Yoon; Sun Young Lee; In Kwon Lee; Henry Kang

doi:10.1007/s11042-012-1242-6

Optimized image resizing using flow-guided seam carving and an interactive genetic algorithm

Jong Chul Yoon, Sun Young Lee, In Kwon Lee, Henry Kang

College of Computing

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

5 Citations (Scopus)

Abstract

In this paper, we introduce a novel method for content-aware image resizing based on flow-guided seam carving. It extends the existing seam carving framework by replacing the conventional energy field with a "structure-aware" energy field that takes into account the feature orientations in the image. Guided by this new energy field, our approach excels in preserving (i.e., avoiding the distortion of) important structures in the image, such as shape boundaries. We also present a simple user interface to further optimize the resizing result based on the genetic selection process among multiple resizing operators such as scaling, cropping, and flow-guided seam carving. We show that such simple user interaction, coupled with the genetic algorithm, dramatically increases the chances of producing the user-desired outcome.

Original language	English
Pages (from-to)	1013-1031
Number of pages	19
Journal	Multimedia Tools and Applications
Volume	71
Issue number	3
DOIs	https://doi.org/10.1007/s11042-012-1242-6
Publication status	Published - 2014 Aug

Bibliographical note

Funding Information:
Acknowledgement This study was supported by 2011 Research Grant form Kangwon National University and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2011-0028568).

All Science Journal Classification (ASJC) codes

Software
Media Technology
Hardware and Architecture
Computer Networks and Communications

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10.1007/s11042-012-1242-6

Cite this

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title = "Optimized image resizing using flow-guided seam carving and an interactive genetic algorithm",

abstract = "In this paper, we introduce a novel method for content-aware image resizing based on flow-guided seam carving. It extends the existing seam carving framework by replacing the conventional energy field with a {"}structure-aware{"} energy field that takes into account the feature orientations in the image. Guided by this new energy field, our approach excels in preserving (i.e., avoiding the distortion of) important structures in the image, such as shape boundaries. We also present a simple user interface to further optimize the resizing result based on the genetic selection process among multiple resizing operators such as scaling, cropping, and flow-guided seam carving. We show that such simple user interaction, coupled with the genetic algorithm, dramatically increases the chances of producing the user-desired outcome.",

author = "Yoon, {Jong Chul} and Lee, {Sun Young} and Lee, {In Kwon} and Henry Kang",

note = "Funding Information: Acknowledgement This study was supported by 2011 Research Grant form Kangwon National University and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2011-0028568).",

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doi = "10.1007/s11042-012-1242-6",

language = "English",

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AU - Lee, In Kwon

AU - Kang, Henry

N1 - Funding Information: Acknowledgement This study was supported by 2011 Research Grant form Kangwon National University and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2011-0028568).

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N2 - In this paper, we introduce a novel method for content-aware image resizing based on flow-guided seam carving. It extends the existing seam carving framework by replacing the conventional energy field with a "structure-aware" energy field that takes into account the feature orientations in the image. Guided by this new energy field, our approach excels in preserving (i.e., avoiding the distortion of) important structures in the image, such as shape boundaries. We also present a simple user interface to further optimize the resizing result based on the genetic selection process among multiple resizing operators such as scaling, cropping, and flow-guided seam carving. We show that such simple user interaction, coupled with the genetic algorithm, dramatically increases the chances of producing the user-desired outcome.

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Optimized image resizing using flow-guided seam carving and an interactive genetic algorithm

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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