Abstract
In this paper, a novel approach is presented for motion information retrieval based on a reference index that reduces the number of costly distance computations for similar measures. Due to high dimensionality of motion's features, the nonlinear Principal Component Analysis (PCA) dimension reduction is used. An algorithmic framework is used to approximate the optimal mapping function by a Radial Basis Function (RBF) for handling new data. Then, a reference index is built based on selecting a small set of representative motion clips in the database. This way we can get a candidate set by abandoning most unrelated motion clips to reduce the number of costly similarity measures significantly. Experimental results show that our methods are effective for data processing in large‐scale databases of agriculture informatisation.
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