H.5. Image Processing and Computer Vision
Reza Kharghanian; Zeynab Mohammadpoory
Abstract
The Convolutional Restricted Boltzmann Machine (CRBM) is a generative model that extracts representations from unlabeled data, achieving success in various applications. However, its unsupervised nature may yield suboptimal representations for specific classification tasks. This paper proposes adapting ...
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The Convolutional Restricted Boltzmann Machine (CRBM) is a generative model that extracts representations from unlabeled data, achieving success in various applications. However, its unsupervised nature may yield suboptimal representations for specific classification tasks. This paper proposes adapting k-means clustering to enhance CRBM parameters, aligning features with informative cluster centers. A novel criterion combining generative and soft-K-Means objectives optimizes both cluster centers and CRBM parameters, allowing for continued unsupervised feature learning.Experiments on MNIST, CIFAR10, and three facial expression datasets (JAFFE, KANADE, BU) show that the proposed method enhances the learning process and offers a more informative representation compared to standard and classification CRBM.
M. Sepahvand; F. Abdali-Mohammadi
Abstract
The success of handwriting recognition methods based on digitizer-pen signal processing is mostly dependent on the defined features. Strong and discriminating feature descriptors can play the main role in improving the accuracy of pattern recognition. Moreover, most recognition studies utilize local ...
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The success of handwriting recognition methods based on digitizer-pen signal processing is mostly dependent on the defined features. Strong and discriminating feature descriptors can play the main role in improving the accuracy of pattern recognition. Moreover, most recognition studies utilize local features or sequences of them. Whereas, it has been shown that the combination of global and local features can increase the recognition accuracy. This paper addresses two mentioned topics. First, a new high discriminative local feature, called Rotation Invariant Histogram of Degrees (RIHoD), is proposed for online digitizer-pen handwriting signals. Second, a feature representation layer is proposed, which maps local features into global ones in a new space using some learning kernels. Different aspects of the proposed local feature and learned global feature are analyzed and its efficiency is evaluated in several online handwriting recognition scenarios.
