Relationship between HOF, MBH, convolution layer and sparse coding
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HOF VS ConvNet: HOF descriptor based on the histograms of optical flow orientations. But convolution network also can do this: given a displacement field input, using single convolution layer that containing orientation-sensitive filters, followed by the rectification and pooling layer.
MBH VS ConvNet: MBH can be computed by the histograms of flow gradient orientations; this corresponds to adding an additional convolutional layer, making it a two-layer representation. But ConvNet repeat this by statcking two conv layers.
Sparse coding VS ConvNet: ConvNet can combine a single convolutional and rectification layer to form a sparse coding.
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