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.

Retrieval

Results

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.