A COMPARISON AND EVALUATION OF MOTION INDEXING ...

known as the F -feature sequence of Γ and denote by F [Γ]. If M + 1 is the number
of F -segments of Γ and if Γ (tm) for tm ∈ [1 : T ], where 0 ≤ m ≥ M, then Γ (tm) is a
pose of the m-th segment and F [Γ] = (F (Γ(t0)), F (Γ(t1)), ..., F (Γ(tM))).
In the feature based segmentation, two motions that differ by some deformation
of the time axis will yield the same F -feature sequences. This method is adaptive in a
sense that the number of segments will be based on the selection of feature functions.
Fine feature functions which consists of many components result segmentations with
many short segments, whereas coarse features functions lead to a smaller number of
long segments.
3.1.6 Indexing and Querying of Database
A database Γ consists of motion capture data streams. Indexing can be de-
fined as the retrieval task which can identify motion clips contained in a specified
query. It can be stated more precisely as: The database consists of a collection Γ
= (Γ1, Γ2, · · · · · · , ΓI) of motion capture data streams Γi, i ∈ [1 : I], where I is the
number of documents. To simplify things, it can be assumed that Γ consists of one
large document by concatenating the documents Γ1, Γ2, · · · · · · , ΓI, keeping track of
document boundaries in a supplemental data structure. The query Q consists of
an example motion clip. A feature function F : ℜ 3J → {0, 1} f can be replaced by
specifying the feature vectors obtained during segmentation and use the notation
F [Γ] = w = (w0, w1, · · · · · · , wM) and F [Q] = v = (υ0, υ1, · · · · · · , υN) to denote the
resulting F -feature sequences of Γ and Q, respectively, where M and N denotes the
number of segment numbers for the respective two feature sequences.
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