期刊
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷 37, 期 1, 页码 94-106出版社
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2014.2343229
关键词
Convolutional sparse coding; filter learning; features extraction; separable convolution; segmentation of linear structures; image denoising; convolutional neural networks; tensor decomposition
资金
- EU ERC project MicroNano
Learning filters to produce sparse image representations in terms of overcomplete dictionaries has emerged as a powerful way to create image features for many different purposes. Unfortunately, these filters are usually both numerous and non-separable, making their use computationally expensive. In this paper, we show that such filters can be computed as linear combinations of a smaller number of separable ones, thus greatly reducing the computational complexity at no cost in terms of performance. This makes filter learning approaches practical even for large images or 3D volumes, and we show that we significantly outperform state-of-the-art methods on the curvilinear structure extraction task, in terms of both accuracy and speed. Moreover, our approach is general and can be used on generic convolutional filter banks to reduce the complexity of the feature extraction step.
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