基于多特征擴散方法的顯著性物體檢測

葉鋒; 洪斯婷; 陳家禎; 鄭子華; 劉廣海

doi:10.11999/JEIT170827

基于多特征擴散方法的顯著性物體檢測

doi: 10.11999/JEIT170827

基金項目:

國家自然科學基金(61671077, 61463008)，福建省自然科學基金(2017J01739)，福建省教育廳項目(JA15136)，福建師范大學教學改革研究項目(I201602015)

計量
- 文章訪問數(shù): 1524
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-08-23
- 修回日期: 2018-01-11
- 刊出日期: 2018-05-19

Salient Object Detection via Multi-feature Diffusion-based Method

Funds:

The National Natural Science Foundation of China (61671077, 61463008), The Natural Science Foundation of Fujian Province (2017J01739), The Scientific Research Fund of Fujian Education Department (JA15136), The Teaching Reform Project of Fujian Normal University (I201602015)

摘要

摘要: 現(xiàn)有的大部分基于擴散理論的顯著性物體檢測方法只用了圖像的底層特征來構(gòu)造圖和擴散矩陣，并且忽視了顯著性物體在圖像邊緣的可能性。針對此，該文提出一種基于圖像的多層特征的擴散方法進行顯著性物體檢測。首先，采用由背景先驗、顏色先驗、位置先驗組成的高層先驗方法選取種子節(jié)點。其次，將選取的種子節(jié)點的顯著性信息通過由圖像的底層特征構(gòu)建的擴散矩陣傳播到每個節(jié)點得到初始顯著圖，并將其作為圖像的中層特征。然后結(jié)合圖像的高層特征分別構(gòu)建擴散矩陣，再次運用擴散方法分別獲得中層顯著圖、高層顯著圖。最后，非線性融合中層顯著圖和高層顯著圖得到最終顯著圖。該算法在3個數(shù)據(jù)集MSRA10K，DUT-OMRON和ECSSD上，用3種量化評價指標與現(xiàn)有4種流行算法進行實驗結(jié)果對比，均取得最好的效果。
- 顯著性物體檢測 /
- 擴散方法 /
- 多層特征 /
- 高層先驗
Abstract: Most existing salient object detection methods based on diffusion theory usually only use one feature of image to construct graph and diffusion matrix, and ignore the possibility that salient objects appear at the border regions of the image. In this paper, a diffusion method based on the multi-layer features of image is proposed to detect salient objects. Firstly, the seed nodes are selected by adopting the high-level prior method, which is composed of background prior, color prior, and location prior. Then, the initial saliency map is obtained by propagating the saliency information carried by the selected seed nodes to each nodes via the diffusion matrix constructed by the low-level feature of the image, and used as the middle-level feature of image. The diffusion matrices are re-synthesized again by the middle-level feature and the high-level feature of the image, and the middle-level saliency map and the high-level saliency map are obtained by the diffusion-based method respectively. The final saliency map is obtained by nonlinearly combining the the middle-level and high-level saliency map. Results on three datasets, MSRA10K, DUT-OMRON and ECSSD, show that the proposed method achieves superior performance compared with the four state-of-art methods in terms of three evaluation metrics.
- Salient object detection /
- Diffusion method /
- Multi-layer features /
- High-level prior

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參考文獻(25)

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