A Gaussian distribution is completely determined by its covariance matrix and its mean (a location in space). The covariance matrix of a Gaussian distribution determines the directions and lengths of the axes of its density contours, all of which are ellipsoids.
These four types of mixture models can be illustrated in full generality using the two-dimensional case. In each of these contour plots of the mixture density, two components are located at $(0,0)$ and $(4,5)$ with weights $3/5$ and $2/5$ respectively. The different weights will cause the sets of contours to look slightly different even when the covariance matrices are the same, but the overall shapes of individual contours will still be similar for identical matrices.
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NB These are plots of the actual mixtures, not of the individual components. Because the components are well separated and of comparable weight, the mixture contours closely resemble the component contours (except at low levels where they may distort and merge, as shown in the center of the "tied" plot for instance).
Full means the components may independently adopt any position and shape.
Tied means they have the same shape, but the shape may be anything.
Diagonal means the contour axes are oriented along the coordinate axes, but otherwise the eccentricities may vary between components.
Tied Diagonal is a "tied" situation where the contour axes are oriented along the coordinate axes. (I have added this because initially it was how I misinterpreted "diagonal.")
Spherical is a "diagonal" situation with circular contours (spherical in higher dimensions, whence the name).
This exhibit a gamut from the most general possible mixture to a very specific kind of mixture. Other (fussier) restrictions are possible, especially in higher dimensions where the numbers of parameters grow rapidly. (A covariance matrix in $n$ dimensions is described by $n(n+1)/2$ independent parameters.)