We present an end-to-end learned algorithm for seeded segmentation. Our method is based on the Random Walker algorithm, where we predict the edge weights of the underlying graph using a convolutional neural network. This can be interpreted as learning context-dependent diffusivities for a linear diffusion process. Besides calculating the exact gradient for optimizing these diffusivities, we also propose simplifications that sparsely sample the gradient and still yield competitive results. The proposed method achieves the currently best results on a seeded version of the CREMI neuron segmentation challenge.
Other important seeded segmentation strategies are based on discrete Markov Random Fields @cite_28 that do however not admit the differentiability we crucially need; or on shortest paths similarities that can be computed even more efficiently than the diffusions we use @cite_9 . However, the use of (single) shortest paths to measure the dissimilarity from each pixel to every seed is not as robust as the averaging over many paths that diffusion processes implicitly accomplish @cite_17 .
@cite_28 @cite_9 @cite_17
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Seeded image segmentation is a popular type of supervised image segmentation in computer vision and image processing. Previous methods of seeded image segmentation treat the image as a weighted graph and minimize an energy function on the graph to produce a segmentation. In this paper, we propose to conduct the seeded image segmentation according to the result of a heat diffusion process in which the seeded pixels are considered to be the heat sources and the heat diffuses on the image starting from the sources. After the diffusion reaches a stable state, the image is segmented based on the pixel temperatures. It is also shown that our proposed framework includes the RandomWalk algorithm for image segmentation as a special case which diffuses only along the two coordinate axes. To better control diffusion, we propose to incorporate the attributes (such as the geometric structure) of the image into the diffusion process, yielding an anisotropic diffusion method for image segmentation. The experiments show that the proposed anisotropic diffusion method usually produces better segmentation results. In particular, when the method is tested using the groundtruth dataset of Microsoft Research Cambridge (MSRC), an error rate of 4.42 can be achieved, which is lower than the reported error rates of other state-of-the-art algorithms. Infinite Hidden Markov Random Fields have been proposed for image segmentation as a solution to the problem of automatically determining the number of regions in an image; however, the model does not maintain identity of segmented regions among multiple images. In order to identify segmented regions in images, we developed Hierarchical Dirichlet Process Markov Random Fields. Our model maintains global identification of segmented regions in multiple images by incorporating the idea of hierarchical modeling and automatically determines the number of segmented regions in each image. We show an experimental comparison between the previous model and our proposed model by changing the observation features from RGB value to color histogram features. Many state-of-the-art segmentation algorithms rely on Markov or Conditional Random Field models designed to enforce spatial and global consistency constraints. This is often accomplished by introducing additional latent variables to the model, which can greatly increase its complexity. As a result, estimating the model parameters or computing the best maximum a posteriori (MAP) assignment becomes a computationally expensive task. In a series of experiments on the PASCAL and the MSRC datasets, we were unable to find evidence of a significant performance increase attributed to the introduction of such constraints. On the contrary, we found that similar levels of performance can be achieved using a much simpler design that essentially ignores these constraints. This more simple approach makes use of the same local and global features to leverage evidence from the image, but instead directly biases the preferences of individual pixels. While our investigation does not prove that spatial and consistency constraints are not useful in principle, it points to the conclusion that they should be validated in a larger context. An unsupervised method for segmenting noisy and blurred images is proposed. A Markov random field (MRF) model is used which is robust to degradation. Since this is computationally intensive, a hierarchical distributed genetic algorithm (HDGA) is used which is unsupervised and parallel. Experimental results show that the proposed method is effective at segmenting real images.