Finding suitable image representations for the task at hand is critical in computer vision. Different approaches extending the original Restricted Boltzmann Machine (RBM) model have recently been proposed to offer rotation-invariant feature learning. In this paper, we present an extended novel RBM that learns rotation invariant features by explicitly factorizing for rotation nuisance in 2D image inputs within an unsupervised framework. While the goal is to learn invariant features, our model infers an orientation per input image during training, using information related to the reconstruction error. The training process is regularised by a Kullback-Leibler divergence, offering stability and consistency. We used the γ -score, a measure that calculates the amount of invariance, to mathematically and experimentally demonstrate that our approach indeed learns rotation invariant features. We show that our method outperforms the current state-of-the-art RBM approaches for rotation invariant feature learning on three different benchmark datasets, by measuring the performance with the test accuracy of an SVM classifier. Our implementation is available at https://bitbucket.org/tuttoweb/rotinvrbm.

中文翻译：

---

受限玻尔兹曼机中的无监督旋转分解

为手头的任务找到合适的图像表示对于计算机视觉至关重要。最近提出了扩展原始受限玻尔兹曼机（RBM）模型的不同方法来提供旋转​​不变特征学习。在本文中，我们提出了一种扩展的新颖RBM，它通过在无监督的框架内显式分解2D图像输入中的旋转滋扰来学习旋转不变特征。虽然目标是学习不变特征，但我们的模型会在训练期间使用与重建误差相关的信息来推断每个输入图像的方向。训练过程由Kullback-Leibler分歧进行规范化，从而提供稳定性和一致性。我们使用γ-得分，该度量可计算不变性的量，从数学和实验上证明了我们的方法确实学习了旋转不变特征。我们展示了通过在SVM分类器的测试精度下测量性能，我们的方法优于在三个不同基准数据集上进行旋转不变特征学习的最新RBM方法。我们的实现可在https://bitbucket.org/tuttoweb/rotinvrbm上找到。