This paper proposes an algorithm for automatic
detection of 3D video shots with different perceptual features. The
proposed algorithm is able to identify distinct three-dimensional
visual scenes by detecting 3D video shot boundaries based on
clustering of depth-temporal features. A combination of texture
variation along the temporal dimension and depth variance is
used by K-means clustering to find the stereo frames which
comprised the 3D scene boundaries. An important characteristic
of the proposed algorithm in comparison with others published in
the literature for temporal segmentation of classic 2D video is that
no thresholds are used in the decision processes neither training
data sets. The experimental results show that the proposed
method is capable of achieving high recall (e.g., 0.95) and
precision rate (e.g., 1.0) in video sequences with both sharp and
smooth 3D scene transitions.