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Their method encodes local motion information by taking into account the self-similarity in three neighborhood circles at a particular spatial position.
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We started by comparing the two methods of encoding local context information, using windows or conjunctions as defined above, together with the other features.
To verify the effectiveness and generalization of the proposed local distance transformation, we apply two different linear coding method respectively, i.e., locality-constrained LLC [13] and local soft-assignment coding method (LSC) [19], to encode local distance vectors due to their high efficiency and prominent performance.
In this work, we used lemmas, POS and chunk tags from neighbouring tokens to encode local context through windows, and the concatenation of lemmas and POS tags to encode local context through conjunctions.
We also utilize the LSC method to encode the local distance vector, therefore the impact of neighbor size (i.e., k LSC) will be discussed while the smoothing factor β is fixed as 10.
The proposed method to encode the local variation is founded on the idea of the intensity signals suggested by Ma et al. [25, 26] and the goal is to extract soft variations robust against the degradation factors.
It encodes the local rate of change of the line tangent to the local boundary.
The proposed descriptor encodes variable local neighbour regions in different scales and locations.
Similar to the LBP operator, DBP encodes the local binary pattern.
That is, (13) Interpreted physically, each A1 and A2 pair encodes the local, time invariant dynamics.
When compared with the binary encoding method, this pulse encoding method has obvious advantages.
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