Co-saliency Detection Based on Convolutional Neural Network and Global Optimization
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摘要: 针对目前协同显著性检测问题中存在的协同性较差、误匹配和复杂场景下检测效果不佳等问题，该文提出一种基于卷积神经网络与全局优化的协同显著性检测算法。首先基于VGG16Net构建了全卷积结构的显著性检测网络，该网络能够模拟人类视觉注意机制，从高级语义层次提取一幅图像中的显著性区域；然后在传统单幅图像显著性优化模型的基础上构造了全局协同显著性优化模型。该模型通过超像素匹配机制，实现当前超像素块显著值在图像内与图像间的传播与共享，使得优化后的显著图相对于初始显著图具有更好的协同性与一致性。最后，该文创新性地引入图像间显著性传播约束因子来克服超像素误匹配带来的影响。在公开测试数据集上的实验结果表明，所提算法在检测精度和检测效率上优于目前的主流算法，并具有较强的鲁棒性。Abstract: To solve the problems in current co-saliency detection algorithms, a novel co-saliency detection algorithm is proposed which applies fully convolution neural network and global optimization model. First, a fully convolution saliency detection network is built based on VGG16Net. The network can simulate the human visual attention mechanism and extract the saliency region in an image from the semantic level. Second, based on the traditional saliency optimization model, the global co-saliency optimization model is constructed, which realizes the transmission and sharing of the current superpixel saliency value in inter-images and intra-image through superpixel matching, making the final saliency map has better co-saliency value. Third, the inter-image saliency value propagation constraint parameter is innovatively introduced to overcome the disadvantages of superpixel mismatching. Experimental results on public test datasets show that the proposed algorithm is superior over current state-of-the-art methods in terms of detection accuracy and detection efficiency, and has strong robustness.
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Key words:
- Co-saliency /
- Deep Learning /
- Convolutional Neural Network /
- Global Optimization
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表 1 不同算法在两大数据库上的测试结果对比
算法 ImgPair iCoSeg AUC AF MAE AUC AF MAE SA 0.967 0.826 0.160 0.965 0.720 0.160 HS 0.954 0.821 0.147 0.954 0.640 0.180 CB-C 0.931 0.782 0.178 0.913 0.647 0.198 CB-S 0.927 0.749 0.181 0.935 0.688 0.173 ACM 0.880 0.719 0.197 – – – SM 0.879 0.724 0.166 0.621 0.580 0.234 LDW – – – 0.957 0.699 0.178 IPIM – – – 0.964 0.703 0.159 本文CNN 0.958 0.811 0.098 0.932 0.761 0.081 本文CNN+COOPT 0.981 0.904 0.075 0.962 0.848 0.056 
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表 2 不同协同显著性算法平均运算时间比较
算法 CB-C SA SM 本文CNN 本文CNN+COOPT 时间(s) 5.40 2.10 6.60 0.12 2.70 处理器 CPU CPU CPU GPU CPU+GPU
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