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Automatic salient object segmentation using saliency map and color segmentation

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Abstract

A new method for automatic salient object segmentation is presented. Salient object segmentation is an important research area in the field of object recognition, image retrieval, image editing, scene reconstruction, and 2D/3D conversion. In this work, salient object segmentation is performed using saliency map and color segmentation. Edge, color and intensity feature are extracted from mean shift segmentation (MSS) image, and saliency map is created using these features. First average saliency per segment image is calculated using the color information from MSS image and generated saliency map. Then, second average saliency per segment image is calculated by applying same procedure for the first image to the thresholding, labeling, and hole-filling applied image. Thresholding, labeling and hole-filling are applied to the mean image of the generated two images to get the final salient object segmentation. The effectiveness of proposed method is proved by showing 80%, 89% and 80% of precision, recall and F-measure values from the generated salient object segmentation image and ground truth image.

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Authors and Affiliations

  1. Department of Plasmabiodisplay, Kwangwoon University, Seoul, 139-701, Korea

    Sung-ho Han

  2. Department of General Education, Kwangwoon University, Seoul, 139-701, Korea

    Gye-dong Jung & Sang-hun Lee

  3. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, 215123, China

    Sangh-yuk Lee

  4. Department of Hospital Management, International University of Korea, Jinju, 660-789, Korea

    Yeong-pyo Hong

Authors
  1. Sung-ho Han
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  2. Gye-dong Jung
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  3. Sangh-yuk Lee
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  4. Yeong-pyo Hong
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  5. Sang-hun Lee
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Corresponding author

Correspondence to Sangh-yuk Lee.

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Han, Sh., Jung, Gd., Lee, Sy. et al. Automatic salient object segmentation using saliency map and color segmentation. J. Cent. South Univ. 20, 2407–2413 (2013). https://doi.org/10.1007/s11771-013-1750-1

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  • DOI: https://doi.org/10.1007/s11771-013-1750-1

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