The unary term ψ(l i ) measures the cost of assigning label l i to superpixel i, and the pairwise term ϕ ij (l i,l j ) measures the penalty of different assignment between similar adjacent superpixels i and j.
The execution of spectral clustering produced the spectral embedding of the superpixels (a new geometric representation of the superpixels), which is given by mapping the superpixels according to the first l eigenvectors of the Laplacian associated with the network.
In practice, we use the k-means algorithm to cluster N superpixels to K clusters via the value of superpixel t.
Maps to the stars' homes?
Press "Maps" to open the Maps application.
The way to use the majority voting principle to classify superpixel blocks is as follows: Firstly, each superpixel block is considered as a set, and the pixels in the set are classified by SVM to obtain the multiple labels.
In our approach, we give the corresponding hierarchical depth map after the superpixel information.
The approach relied on the normalized cut algorithm to classify the image in order to obtain the superpixel map, from which the local variance was computed and selected.
