Random Forest With Learned Representations for Semantic Segmentation
Random Forest With Learned Representations for Semantic Segmentation
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DOI：
10.1109/tip.2019.2905081
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发表时间：
2019-07-01
影响因子：
10.6
通讯作者：
Nguyen, Truong Q.
中科院分区：
文献类型：
--
作者：
Kang, Byeongkeun;Nguyen, Truong Q.
我们提出了一个随机森林框架，学习的权重，形状和稀疏的特征表示实时语义分割。典型的过滤器（内核）具有预定的形状和稀疏性，并且只学习权重。一些特征提取方法固定权重，只学习形状和稀疏性。这些预定的约束限制了学习和提取最佳特征。为了克服这一限制，我们提出了一种无约束的表示，能够通过学习权重，形状和稀疏度来提取最佳特征。然后，我们提出了随机森林框架，学习灵活的过滤器使用迭代优化算法和段输入图像使用学习的表示。我们证明了所提出的方法的有效性，使用一个手分割数据集的手对象交互和使用两个语义分割数据集。实验结果表明，该方法利用有限的计算和内存资源实现了实时语义分割。
We present a random forest framework that learns the weights, shapes, and sparsities of feature representations for real-time semantic segmentation. Typical filters (kernels) have predetermined shapes and sparsities and learn only weights. A few feature extraction methods fix weights and learn only shapes and sparsities. These predetermined constraints restrict learning and extracting optimal features. To overcome this limitation, we propose an unconstrained representation that is able to extract optimal features by learning weights, shapes, and sparsities. We then present the random forest framework that learns the flexible filters using an iterative optimization algorithm and segments input images using the learned representations. We demonstrate the effectiveness of the proposed method using a hand segmentation dataset for hand-object interaction and using two semantic segmentation datasets. The results show that the proposed method achieves real-time semantic segmentation using limited computational and memory resources.