
Learning, analysis, and detection of motion in the framework of a hierarchical compositional visual architecture
Table above shows the results, obtained on UCF Sports dataset (http://crcv.ucf.edu/data/UCF_Sports_Action.php). We report recognition rate with respect to the number...
The developed algorithms are computationally effective and the compositional processing pipeline is well-suited for implementation on massively parallel architectures. Many...
Our model is comprised of three processing stages, as shown in the Figure. The task of the lowest stage (layers...
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Layer L1 provides an input to the compositional hierarchy. Motion, obtained in L0 is encoded using a small dictionary.
Our model is comprised of three processing stages, as shown in the Figure. The task of the lowest stage (layers L0 and L1) is the extraction of relatively basic motion features. The middle stage implements hierarchical compositional structure, and consists of multiple layers, L2 to LN. The layered structure decreases the complexity of the learning process due to limited receptive field in which the neighborhood of each element is observed. In particular, such a scheme avoids the need to jointly estimate a rather large number of parameters for all layers by decomposing the learning into sequence of layer-wise training epochs. The topmost stage provides discriminative capabilities using SVM classification on the outputs of the middle, compositional stage. Each layer Li is associated with its dictionary, i. While the 1 is fixed, the higher layer dictionaries are obtained through learning.
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