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Unsupervised visual learning of three-dimensional objects using a modular network architecture
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DOI：10.1016/S0893-6080(99)00052-0.png)
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摘要
En 中文
This paper presents a modular network architecture that learns to cluster multiple views of multiple three-dimensional (3D) objects. The proposed network model is based on a mixture of non-linear autoencoders, which compete to encode multiple views of each 3D object. The main advantage of using a mixture of autoencoders is that it can capture multiple non-linear sub-spaces, rather than multiple centers for describing complex shapes of the view distributions. The unsupervised training algorithm is formulated within a maximum-likelihood estimation framework. The performance of the modular network model is evaluated through experiments using synthetic 3D wire-frame objects and gray-level images of real 3D objects. It is shown that the performance of the modular network model is superior to the performance of the conventional clustering algorithms, such as the K-means algorithm and the Gaussian mixture model. (C) 1999 Elsevier Science Ltd. All rights reserved.
Keyword:
three-dimensional object recognition
unsupervised learning
clustering
modular networks
mixture of autoencoders
multiple views
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6.3
论文数:
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被引数:
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