Image Feature Extraction and Similarity Evaluation using Higher-Order Moment Kernels (ニューロコンピューティング) Image Feature Extraction and Similarity Evaluation using Higher-Order Moment Kernels
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The Higher-Order Moment (HOM) kernel has been known to enable efficient utilization of higher-order moment (statistical) features in signals and images. Previously, we have reported that support vector machines (SVMs) that employ the kernel can classify image textures utilizing the HOM features efficiently. However, the actual conditions for using the discretized version of the kernel for image feature extraction have not been made clear yet. In this work, properties of the discretized HOM kernel against image fluctuations such as scaling, rotation, phase shift and additive noise are experimentally investigated.
- IEICE technical report. Neurocomputing
IEICE technical report. Neurocomputing 111(483), 393-398, 2012-03-07
The Institute of Electronics, Information and Communication Engineers