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This is followed by computing a "feature skeleton" on the mesh that determines the primary regions of the object.
Figure 1 Unsupervised action recognition is performed by computing a feature vector for each frame of the sequence (left), as described in Section 6.
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The metafeatures are found for this method by computing a feature-label co-occurrence value for each feature from the source and target space by calculating the expected value of a feature based on the given label from the labeled training data.
SURF [45]: this method efficiently detects features by computing a rough approximation of the Hessian matrix using integral images.
A new, substantially different, image is estimated from an input image by computing a number of filtered input images (feature images) and mapping these to the desired output for every pixel in the image.
The nonlinear implicit mapping provided by the kernel operation allows us to generate an inner product in the feature space by computing a kernel function that has arguments taken from the input space.
Firstly it trains the SVM classifier, followed by computing a specific ranking criterion for all features.
Then we iteratively refine an initial saliency map derived from background estimation by computing the feature contrast only within local surrounding area whose range and shape are changed adaptively.
However, feature selection algorithms usually require a calculation of redundancy measures, for example, by computing correlations of a feature's values across samples [9].
We start by computing the features of each training sample in a class first.
Once trained, the classifier will be applied to unlabeled data sets by computing these features and inputting them into the classifier to retrieve a set of boundary probabilities at each pixel (see Fig. 2).
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