基于多模態(tài)特征融合監(jiān)督的RGB-D圖像顯著性檢測
doi: 10.11999/JEIT190297
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安徽大學(xué)計算機科學(xué)與技術(shù)學(xué)院 合肥 230601
RGB-D Image Saliency Detection Based on Multi-modal Feature-fused Supervision
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School of Computer Science and Technology, Anhui University, Hefei 230601, China
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摘要:
RGB-D圖像顯著性檢測是在一組成對的RGB和Depth圖中識別出視覺上最顯著突出的目標(biāo)區(qū)域。已有的雙流網(wǎng)絡(luò)，同等對待多模態(tài)的RGB和Depth圖像數(shù)據(jù)，在提取特征方面幾乎一致。然而，低層的Depth特征存在較大噪聲，不能很好地表征圖像特征。因此，該文提出一種多模態(tài)特征融合監(jiān)督的RGB-D圖像顯著性檢測網(wǎng)絡(luò)，通過兩個獨立流分別學(xué)習(xí)RGB和Depth數(shù)據(jù)，使用雙流側(cè)邊監(jiān)督模塊分別獲取網(wǎng)絡(luò)各層基于RGB和Depth特征的顯著圖，然后采用多模態(tài)特征融合模塊來融合后3層RGB和Depth高維信息生成高層顯著預(yù)測結(jié)果。網(wǎng)絡(luò)從第1層至第5層逐步生成RGB和Depth各模態(tài)特征，然后從第5層到第3層，利用高層指導(dǎo)低層的方式產(chǎn)生多模態(tài)融合特征，接著從第2層到第1層，利用第3層產(chǎn)生的融合特征去逐步地優(yōu)化前兩層的RGB特征，最終輸出既包含RGB低層信息又融合RGB-D高層多模態(tài)信息的顯著圖。在3個公開數(shù)據(jù)集上的實驗表明，該文所提網(wǎng)絡(luò)因為使用了雙流側(cè)邊監(jiān)督模塊和多模態(tài)特征融合模塊，其性能優(yōu)于目前主流的RGB-D顯著性檢測模型，具有較強的魯棒性。
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關(guān)鍵詞:
- RGB-D顯著性檢測 /
- 卷積神經(jīng)網(wǎng)絡(luò) /
- 多模態(tài) /
- 監(jiān)督
Abstract:RGB-D saliency detection identifies the most visually attentive target areas in a pair of RGB and Depth images. Existing two-stream networks, which treat RGB and Depth data equally, are almost identical in feature extraction. As the lower layers Depth features with a lot of noise, it causes image features not be well characterized. Therefore, a multi-modal feature-fused supervision of RGB-D saliency detection network is proposed, RGB and Depth data are studied independently through two-stream , double-side supervision module is used respectively to obtain saliency maps of each layer, and then the multi-modal feature-fused module is used to later three layers of the fused RGB and Depth of higher dimensional information to generate saliency predicted results. Finally, the information of lower layers is fused to generate the ultimate saliency maps. Experiments on three open data sets show that the proposed network has better performance and stronger robustness than the current RGB-D saliency detection models.
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Key words:
- RGB-D saliency detection /
- Convolutional Neural Network(CNN) /
- Multi-modal /
- Supervision
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表 1 在F-measure, MAE, S-measure, E-measure上與其他模型的對比
算法 NLPR1000 NJU2000 STEREO F MAE S E F MAE S E F MAE S E TAN 0.7956 0.0410 0.8861 0.9161 0.8442 0.0605 0.8785 0.8932 0.8489 0.0591 0.8775 0.9108 PCFN 0.7948 0.0437 0.8736 0.9163 0.8440 0.0591 0.8770 0.8966 0.8450 0.0606 0.8800 0.9054 MMCI 0.7299 0.0591 0.8557 0.8717 0.8122 0.0790 0.8581 0.8775 0.8120 0.0796 0.8599 0.8896 DF 0.7348 0.0891 0.7909 0.8600 0.7703 0.1406 0.7596 0.8383 0.7650 0.1395 0.7664 0.8438 本文模型 0.8629 0.0318 0.9117 0.9464 0.8578 0.0541 0.8852 0.8956 0.8622 0.0519 0.8894 0.9130 
下載: 導(dǎo)出CSV
表 2 雙流側(cè)邊監(jiān)督模塊有效性實驗對比結(jié)果
算法 NLPR1000 NJU2000 STEREO F MAE S E F MAE S E F MAE S E NDS 0.8358 0.0340 0.9085 0.9336 0.8502 0.0568 0.8848 0.8902 0.8524 0.0552 0.8879 0.9066 本文模型(DS) 0.8629 0.0318 0.9117 0.9464 0.8578 0.0541 0.8852 0.8956 0.8622 0.0519 0.8894 0.9130
下載: 導(dǎo)出CSV
表 3 多尺度模塊有效性實驗對比結(jié)果
算法 NLPR1000 NJU2000 STEREO F MAE S E F MAE S E F MAE S E BN 0.8488 0.0340 0.9059 0.9398 0.8504 0.0566 0.8814 0.8928 0.8573 0.0547 0.8848 0.9093 本文模型 0.8629 0.0318 0.9117 0.9464 0.8578 0.0541 0.8852 0.8956 0.8622 0.0519 0.8894 0.9130
下載: 導(dǎo)出CSV
表 4 低維Depth特征實驗對比結(jié)果
算法 NLPR1000 NJU2000 STEREO F MAE S E F MAE S E F MAE S E DY 0.8715 0.1087 0.8187 0.9479 0.8250 0.1310 0.8414 0.8785 0.8355 0.1277 0.8541 0.8984 本文模型 0.8629 0.0318 0.9117 0.9464 0.8578 0.0541 0.8852 0.8956 0.8622 0.0519 0.8894 0.9130
下載: 導(dǎo)出CSV
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