We consider the task of pixel-wise semantic segmentation given a small set of labelled training images. Among two of the most popular techniques to address this task are Random Forests (RF) and Neural Networks (NN). The main contribution of this work is to explore the relationship between two special forms of these techniques: cascaded RFs and deep Convolutional Neural Networks (CNN). We show that there exists an (approximate) mapping from cascaded RF to deep CNN, and back. This insight gives two major practical benefits: a) the performance of a greedily trained cascaded RF can be improved; b) a deep CNN can be intelligently constructed and initialized. Furthermore, the resulting CNN architecture has not yet been explored for pixel-wise semantic labelling. We experimentally verify these practical benefits for the task of densely labelling segments of the developing zebrafish body plan in microscopy images. Recently, numerous groups have explored the use of Convolutional Neural Networks (CNNs) for semantic segmentation [7, 17, 6, 29] , which has the advantage that it enables "end-to-end learning" of all model parameters. This trend is largely inspired by the success of deep CNNs on high-level computer vision tasks, such as image classification [14] and object detection [11] . However, deep CNNs require substantial experience and large labeled data sets to train [2, 3] . Thus, there currently exists a divide between cascaded classifiers, which perform well on semantic segmentation and can be trained with limited labelled data, and deep CNNs, which have recently been adapted to this task, and can dominate performance given enough training data. Here, we propose an intermediate solution, exploiting the fundamental connection between decision trees (DT) and NNs [24] to bridge the gap between cascaded classifiers and deep CNNs. This provides a novel approach with the strengths of cascaded classifiers, namely robustness to limited training data, and the end-end-learning capacity of neural networks. Contribution. Building upon the works of Sethi [24] and Welbl [28] , we demonstrate that a cascaded RF is a special case of a deep CNN, with many sparse convolution kernels. To this end we describe a arXiv:1507.07583v1 [cs.CV]
Richmond et al. REF explored the mapping of stacked RFs to CNNs and an approximate mapping back to perform semantic segmentation.
Relating Cascaded Random Forests to Deep Convolutional Neural Networks for Semantic Segmentation
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Computer Science