391 research outputs found
CRF Learning with CNN Features for Image Segmentation
Conditional Random Rields (CRF) have been widely applied in image
segmentations. While most studies rely on hand-crafted features, we here
propose to exploit a pre-trained large convolutional neural network (CNN) to
generate deep features for CRF learning. The deep CNN is trained on the
ImageNet dataset and transferred to image segmentations here for constructing
potentials of superpixels. Then the CRF parameters are learnt using a
structured support vector machine (SSVM). To fully exploit context information
in inference, we construct spatially related co-occurrence pairwise potentials
and incorporate them into the energy function. This prefers labelling of object
pairs that frequently co-occur in a certain spatial layout and at the same time
avoids implausible labellings during the inference. Extensive experiments on
binary and multi-class segmentation benchmarks demonstrate the promise of the
proposed method. We thus provide new baselines for the segmentation performance
on the Weizmann horse, Graz-02, MSRC-21, Stanford Background and PASCAL VOC
2011 datasets
Learning Graph Cut Energy Functions for Image Segmentation
In this paper we address the task of learning how to segment a particular class of objects, by means of a training set of images and their segmentations. In particular we propose a method to overcome the extremely high training time of a previously proposed solution to this problem, Kernelized Structural Support Vector Machines. We employ a one-class SVM working with joint kernels to robustly learn significant support vectors (representative image-mask pairs) and accordingly weight them to build a suitable energy function for the graph cut framework. We report results obtained on two public datasets and a comparison of training times on different training set sizes
ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation
We propose a structured prediction architecture, which exploits the local
generic features extracted by Convolutional Neural Networks and the capacity of
Recurrent Neural Networks (RNN) to retrieve distant dependencies. The proposed
architecture, called ReSeg, is based on the recently introduced ReNet model for
image classification. We modify and extend it to perform the more challenging
task of semantic segmentation. Each ReNet layer is composed of four RNN that
sweep the image horizontally and vertically in both directions, encoding
patches or activations, and providing relevant global information. Moreover,
ReNet layers are stacked on top of pre-trained convolutional layers, benefiting
from generic local features. Upsampling layers follow ReNet layers to recover
the original image resolution in the final predictions. The proposed ReSeg
architecture is efficient, flexible and suitable for a variety of semantic
segmentation tasks. We evaluate ReSeg on several widely-used semantic
segmentation datasets: Weizmann Horse, Oxford Flower, and CamVid; achieving
state-of-the-art performance. Results show that ReSeg can act as a suitable
architecture for semantic segmentation tasks, and may have further applications
in other structured prediction problems. The source code and model
hyperparameters are available on https://github.com/fvisin/reseg.Comment: In CVPR Deep Vision Workshop, 201
Learning Discriminative Features and Structured Models for Segmentation in Microscopy and Natural Images
Segmenting images is a significant challenge that has drawn a lot of attention from different fields of artificial intelligence and has many practical applications. One such challenge addressed in this thesis is the segmentation of electron microscope (EM) imaging of neural tissue. EM microscopy is one of the key tools used to analyze neural tissue and understand the brain, but the huge amounts of data it produces make automated analysis necessary. In addition to the challenges specific to EM data, the common problems encountered in image segmentation must also be addressed. These problems include extracting discriminative features from the data and constructing a statistical model using ground-truth data. Although complex models appear to be more attractive because they allow for more expressiveness, they also lead to a higher computational complexity. On the other hand, simple models come with a lower complexity but less faithfully express the real world. Therefore, one of the most challenging tasks in image segmentation is in constructing models that are expressive enough while remaining tractable. In this work, we propose several automated graph partitioning approaches that address these issues. These methods reduce the computational complexity by operating on supervoxels instead of voxels, incorporating features capable of describing the 3D shape of the target objects and using structured models to account for correlation in output variables. One of the non-trivial issues with such models is that their parameters must be carefully chosen for optimal performance. A popular approach to learning model parameters is a maximum-margin approach called Structured SVM (SSVM) that provides optimality guarantees but also suffers from two main drawbacks. First, SSVM-based approaches are usually limited to linear kernels, since more powerful nonlinear kernels cause the learning to become prohibitively expensive. In this thesis, we introduce an approach to “kernelize” the features so that a linear SSVM framework can leverage the power of nonlinear kernels without incurring their high computational cost. Second, the optimality guarentees are violated for complex models with strong inter-relations between the output variables. We propose a new subgradient-based method that is more robust and leads to improved convergence properties and increased reliability. The different approaches presented in this thesis are applicable to both natural and medical images. They are able to segment mitochondria at a performance level close to that of a human annotator, and outperform state-of-the-art segmentation techniques while still benefiting from a low learning time
A Multi-Plane Block-Coordinate Frank-Wolfe Algorithm for Training Structural SVMs with a Costly max-Oracle
Structural support vector machines (SSVMs) are amongst the best performing
models for structured computer vision tasks, such as semantic image
segmentation or human pose estimation. Training SSVMs, however, is
computationally costly, because it requires repeated calls to a structured
prediction subroutine (called \emph{max-oracle}), which has to solve an
optimization problem itself, e.g. a graph cut.
In this work, we introduce a new algorithm for SSVM training that is more
efficient than earlier techniques when the max-oracle is computationally
expensive, as it is frequently the case in computer vision tasks. The main idea
is to (i) combine the recent stochastic Block-Coordinate Frank-Wolfe algorithm
with efficient hyperplane caching, and (ii) use an automatic selection rule for
deciding whether to call the exact max-oracle or to rely on an approximate one
based on the cached hyperplanes.
We show experimentally that this strategy leads to faster convergence to the
optimum with respect to the number of requires oracle calls, and that this
translates into faster convergence with respect to the total runtime when the
max-oracle is slow compared to the other steps of the algorithm.
A publicly available C++ implementation is provided at
http://pub.ist.ac.at/~vnk/papers/SVM.html
Integrated Inference and Learning of Neural Factors in Structural Support Vector Machines
Tackling pattern recognition problems in areas such as computer vision,
bioinformatics, speech or text recognition is often done best by taking into
account task-specific statistical relations between output variables. In
structured prediction, this internal structure is used to predict multiple
outputs simultaneously, leading to more accurate and coherent predictions.
Structural support vector machines (SSVMs) are nonprobabilistic models that
optimize a joint input-output function through margin-based learning. Because
SSVMs generally disregard the interplay between unary and interaction factors
during the training phase, final parameters are suboptimal. Moreover, its
factors are often restricted to linear combinations of input features, limiting
its generalization power. To improve prediction accuracy, this paper proposes:
(i) Joint inference and learning by integration of back-propagation and
loss-augmented inference in SSVM subgradient descent; (ii) Extending SSVM
factors to neural networks that form highly nonlinear functions of input
features. Image segmentation benchmark results demonstrate improvements over
conventional SSVM training methods in terms of accuracy, highlighting the
feasibility of end-to-end SSVM training with neural factors
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