Training products of experts by minimizing contrastive divergence
Neural Computation
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A fast learning algorithm for deep belief nets
Neural Computation
Detection of mine-like objects using restricted boltzmann machines
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
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Advances in high frequency sonar have provided increasing resolution of sea bottom objects, providing higher fidelity sonar data for automated target recognition tools. Here we investigate if advanced techniques in the field of visual object recognition and machine learning can be applied to classify mine-like objects from such sonar data. In particular, we investigate if the recently popular Scale-Invariant Feature Transform (SIFT) can be applied for such high-resolution sonar data.We also follow up our previous approach in applying the unsupervised learning of deep belief networks, and advance our methods by applying a convolutional Restricted Boltzmann Machine (cRBM). Finally, we now use Support Vector Machine (SVM) classifiers on these learned features for final classification. We find that the cRBM-SVM combination slightly outperformed the SIFT features and yielded encouraging performance in comparison to state-of-the-art, highly engineered template matching methods.