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基于帶匯點(diǎn)Laplace擴(kuò)散模型的顯著目標(biāo)檢測(cè)

王寶艷,  張鐵,  王新剛

王寶艷, 張鐵, 王新剛. 基于帶匯點(diǎn)Laplace擴(kuò)散模型的顯著目標(biāo)檢測(cè)[J]. 電子與信息學(xué)報(bào), 2017, 39(8): 1934-1941. doi: 10.11999/JEIT161296
引用本文: 王寶艷, 張鐵, 王新剛. 基于帶匯點(diǎn)Laplace擴(kuò)散模型的顯著目標(biāo)檢測(cè)[J]. 電子與信息學(xué)報(bào), 2017, 39(8): 1934-1941. doi: 10.11999/JEIT161296
WANG Baoyan, ZHANG Tie, WANG Xingang. Salient Object Detection Based on Laplace Diffusion Models with Sink Points[J]. Journal of Electronics & Information Technology, 2017, 39(8): 1934-1941. doi: 10.11999/JEIT161296
Citation: WANG Baoyan, ZHANG Tie, WANG Xingang. Salient Object Detection Based on Laplace Diffusion Models with Sink Points[J]. Journal of Electronics & Information Technology, 2017, 39(8): 1934-1941. doi: 10.11999/JEIT161296

基于帶匯點(diǎn)Laplace擴(kuò)散模型的顯著目標(biāo)檢測(cè)

doi: 10.11999/JEIT161296
  • 1.

    (東北大學(xué)信息科學(xué)與工程學(xué)院 沈陽 110819) ②(東北大學(xué)理學(xué)院 沈陽 110819) ③(東北大學(xué)秦皇島分?？刂乒こ虒W(xué)院 秦皇島 066004)

基金項(xiàng)目: 

國(guó)家自然科學(xué)基金(51475086)，遼寧省自然科學(xué)基金(2014020026)

Salient Object Detection Based on Laplace Diffusion Models with Sink Points

  • 1.

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

  • 2.

    (College of Science, Northeastern University, Shenyang 110819, China)

Funds: 

The National Natural Science Foundation of China (51475086), The Natural Science Foundation of Liaoning Province (2014020026)

  • 摘要: 該文基于Laplace相似度量的構(gòu)造方法，針對(duì)兩階段顯著目標(biāo)檢測(cè)中顯著種子的不同類型(稀疏或稠密)，提出了相應(yīng)的顯著性擴(kuò)散模型，從而實(shí)現(xiàn)了基于擴(kuò)散的兩階段互補(bǔ)的顯著目標(biāo)檢測(cè)。尤其是第2階段擴(kuò)散模型中匯點(diǎn)的融入，一方面更好地抑制了顯著性圖中的背景，同時(shí)對(duì)于控制因子的取值更加穩(wěn)健。實(shí)驗(yàn)結(jié)果表明，當(dāng)顯著種子確定時(shí)，不同的擴(kuò)散模型會(huì)導(dǎo)致顯著性擴(kuò)散程度的差異?；趲R點(diǎn)Laplace的兩階段互補(bǔ)的擴(kuò)散模型較其他擴(kuò)散模型更有效、更穩(wěn)健。同時(shí)，從多項(xiàng)評(píng)價(jià)指標(biāo)分析，該算法與目前流行的5種顯著目標(biāo)檢測(cè)算法相比，具有較大優(yōu)勢(shì)。這表明此種用于圖像檢索或分類的Laplace相似度量的構(gòu)造方法在顯著目標(biāo)檢測(cè)中也是適用的。
    Abstract: Based on Laplace similarity metrics, corresponding diffusion-based saliency models are proposed according to different clusters (sparse or dense) of salient seeds in the two-stage detection, a diffusion-based two-stage complementary method for salient object detection is therefore investigated. Especially for the introduction of sink points in the second stage, saliency maps obtained by this proposed method can well restrain background parts, as well as become more robust with the change of control factor. Experiments show that different diffusion models will cause diversities of saliency diffusion degree when salient seeds are determined. In addition, the two-stage Laplace-based diffusion model with sink points is more effective and robust than other two-stage diffusion models. Meanwhile, the proposed algorithm is superior over the existing five state-of-the-art methods in terms of different metrics. This exactly shows that the similarity metrics method applied to image retrieval and classification is also available for salient objects detection.
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出版歷程
  • 收稿日期:  2016-11-28
  • 修回日期:  2017-04-25
  • 刊出日期:  2017-08-19

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