A COMPARISON AND EVALUATION OF MOTION INDEXING ...

CHAPTER 3
FEATURE-BASED TECHNIQUE
This chapter describes the overview and implementation of feature function
based indexing technique. This method has been mentioned in the reserach paper,
Efficient content-based retrieval of motion capture data[15] and the book, Information
Retrieval for Music and Motion[16].
Although there is a massive increase in the use of motion capture data, there
still is no efficient motion retrieval systems that allow identifying and extracting
user-specified motions. Large databases containing time series data require efficient
indexing and retrieval methods. Previously, retrieval systems were based on manually
generated textual annotations, which roughly describe the motions in words. For large
datasets, the manual generation of reliable and descriptive labels becomes infeasible.
When the motion is identified and extracted on the basis of semantic description of
the motion, it is known as content-based motion retrieval. However, the different
motions are logically related and distributed randomly in the dataset. The motion
specification can be done in query mode in which a user provides the description of
the motion in the form of a small motion example. The important aspect in motion
retrieval is the notion of similarity used to compare the query with the motions in
the database. Two motions may be regarded as similar if they represent variations of
the same action or sequence of actions. These variations may concern the spatial as
well as the temporal domain.
In general, similar motions need not be numerically similar. Motion capture
data has a richer semantic content because the position and the orientation of all
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