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Improved accuracy of superpixel segmentation by region merging method

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Abstract

Superpixel as an important pre-processing technique has been successfully used in many vision applications. In this paper, we proposed a region merging method to improve superpixel segmentation accuracy with low computational cost. We first segmented the image into many accurate small regions, and then progressively agglomerated them until the desired region number was reached. The region merging weight was derived from a novel energy function, which encourages the superpixel with color consistency and similar size. Experimental results on the Berkeley BSDS500 data set showed that our region merging method can significantly improve the accuracy of superpixel segmentation. Moreover, the region merging method only need 50 ms to process a 481 × 321 image on a single Intel i3 CPU at 2.5 GHz.

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Authors and Affiliations

  1. School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, 430074, China

    Song Zhu, Danhua Cao, Yubin Wu & Shixiong Jiang

Authors
  1. Song Zhu
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  2. Danhua Cao
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  3. Yubin Wu
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  4. Shixiong Jiang
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Corresponding author

Correspondence to Danhua Cao.

Additional information

Song Zhu is a Ph.D. candidate in the School of Optical and Electronic Information, Huazhong University of Science and Technology (HUST). He received his B.E. degree in the School of Optical and Electronic Information, HUST, 2010. His research interests include 3D computer vision, image segment and pattern recognition.

Danhua Cao is a professor in the School of Optical and Electronic Information, Huazhong University of Science and Technology. She received her Ph.D. degree in electronic physics and devices, HUST, 1993; B.E. degree in measuring and control technology and instrumentations, HUST, 1987. She is the permanent member of the Professional Committee of Opto-Electronic Technology in the Chinese Optical Society. Her research interests include optoelectronic sensing and signal processing, machine vision algorithm and systems.

Yubin Wu is an associate professor in the School of Optical and Electronic Information, Huazhong University of Science and Technology (HUST). He received his M.E. degree in optical engineering from Institute of Optics and Electronics of the Chinese Academy of Sciences, 1987; B.E. degree in optical instruments, HUST, 1984. His research interests include optoelectronic sensing and signal processing, machine vision, and the development of high-tech products.

Yubin Wu is an associate professor in the School of Optical and Electronic Information, Huazhong University of Science and Technology (HUST). He received his M.E. degree in optical engineering from Institute of Optics and Electronics of the Chinese Academy of Sciences, 1987; B.E. degree in optical instruments, HUST, 1984. His research interests include optoelectronic sensing and signal processing, machine vision, and the development of high-tech products.

Shixiong Jiang is a Ph.D. candidate in the School of Optical and Electronic Information, Huazhong University of Science and Technology (HUST). He received his B.E. degree in the School of Optical and Electronic Information, HUST, 2011. His research interests include machine vision, 3D reconstruction and pattern recognition.

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Zhu, S., Cao, D., Wu, Y. et al. Improved accuracy of superpixel segmentation by region merging method. Front. Optoelectron. 9, 633–639 (2016). https://doi.org/10.1007/s12200-015-0482-2

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  • DOI: https://doi.org/10.1007/s12200-015-0482-2

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