Motion Map: Image-based Retrieval and Segmentation of Motion Data EG SCA ’ 04 學生 : 林家如

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 postures.

Motion Map  Constructing a graphical user interface for motion 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 defined as  model vector, m i,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.

Retrieval

Results

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.