RGBD Image Co-saliency Object Detection Based on Sample Selection
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摘要: 协同显著目标检测的目的是在包含两张及以上相关图像的图像组中检测共同显著的物体。该文提出一种利用机器学习的方法对协同显著目标进行检测。首先，基于4个评分指标从图像组中选择部分显著目标易于检测的简单图像，构成简单图像集；接着，基于协同一致性的原则，从简单图像集中提取正负样本，并用深度学习模型提取的高维语义特征表示正负样本；再者，利用正负样本训练的协同显著分类器对图像中的超像素进行分类，得到协同显著目标区域；最后，经过一个平滑融合的操作，得到最终的协同显著图。在公开数据集上的测试结果表明，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: Co-saliency object detection aims to discover common and salient objects in an image group which contains two or more relevant images. In this paper, a method of using machine learning is proposed to detect co-saliency objects. Firstly, some simple images are selected to form a simple image set based on four scoring indicators. Secondly, positive and negative samples are extracted from the simple images set based on co-coherence characteristics, and high-dimensional semantic features are extracted by the deep learning model which receives RGBD four-channels input. Thirdly, the co-saliency classifier is trained by positive and negative samples, and co-saliency maps are generated by testing all the superpixels in the images by the co-saliency classifier. Finally, a smooth fusion operation is adopted to generate the final co-saliency map. Experimental results on the public benchmark dataset show that the proposed algorithm is superior to the state-of-the-art methods in terms of accuracy and efficiency, and it is robust.
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Key words:
- Object detection /
- Co-saliency object /
- RGBD images /
- Deep learning /
- Classifier
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表 1 不同算法在两个数据集上的测试结果对比
RGBD CoSal150 RGBD CoSeg183 S-measure F-measure MAE S-measure F-measure MAE ESCS 0.625 0.587 0.218 0.636 0.414 0.156 CBCS 0.572 0.582 0.215 0.622 0.365 0.116 ICFS 0.710 0.764 0.179 0.630 0.443 0.163 MCL 0.766 0.810 0.137 0.689 0.488 0.098 本文方法 0.849 0.881 0.089 0.708 0.502 0.081 
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表 2 不同模块在两个数据集上的测试结果对比
RGBD CoSal150 RGBD CoSeg183 S-measure F-measure MAE S-measure F-measure MAE 颜色+纹理特征 0.816 0.817 0.131 0.661 0.473 0.143 无简单图像选择 0.832 0.837 0.117 0.702 0.477 0.090 高维语义特征+简单图像选择 0.849 0.881 0.089 0.708 0.502 0.081
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