Motion Map: Image-based Retrieval and
Segmentation of Motion Data
EG SCA ’04學生 : 林家如 9557057
Outline Introduction Framework Results Conclusions Future Works
Introduction Semantic-based retrieval lacks the capability
of accurately clipping the proper segment of the data.
Provide GUI for retrieving motion data.
Using Self-organizing map (SOM).
Introduction Only need to specify starting and ending postu
res.
Motion Map Constructing a graphical user interface for mot
ion data retrieval.
SOM Self-organizing feature map network. A type of unsupervised learning. Usually 1D or 2D. A mapping that preserves neighborhood
relations. Often used in information visualization.
SOM For each sample posture, an input vector is def
ined as
model vector, mi,j
SOM model vector
Learning-rate:
The width of kernel:
Clustering Divides regions by detecting borders The average difference against 4
neighbors
Create vertical border if
Labeling
Posture Icons From the node that is nearest to the
center of each clustered region.
Trajectory Each motion can be represented as a
trajectory. The walking motion:
Virtual Node Increase the resolution
with small computational cost.
Can be preprocessed for great detail with the cost of storage.
Conclusions Contributions:
Automatically Easily Retrieve Display motion as a trajectory
Defects: Can’t distinguish different performers Can’t reflect the dynamical feature
Future Works Analyzing minute difference.
Zooming in the motion trajectories.
Interactive data editing. Motion blending by drawing an interpolation path
on the map.