A COMPARISON AND EVALUATION OF MOTION INDEXING ...

Segmentation of motion data is followed by indexing of data, query retrieval,
and other processing. The indexing and retrieval of motion data can be described as
a process of identification and extraction of given motion clips. In general, indexing
is based on user semantics and content of the data. Indexing and segmentation based
on data content is an important problem. Different techniques and mathematical
models like motion graphs, Hidden Markov Models, Support Vector Machines were
used for recognition and processing of time series data. Among these techniques, we
have chosen three different techniques for motion data indexing which are described
below
The Principal Component Analysis (PCA) and other dimensionality reduction
techniques are used in modeling and other processing of high dimensional data by
researchers in the field of computer graphics, robotics, computer vision, machine
learning, and biomechanics. As stated in the book “Principal Component Analysis”
by I.T.Jollife, “The central idea of PCA analysis is to reduce the dimensionality of a
data set consisting of large number of interrelated variable, while retaining as much
as possible of the variation present in the data”[12]. The PCA technique transforms
the existing datasets into a new set of variables which are also known as principal
components. These components are uncorrelated and ordered such that the first few
components contain most of the variation of the original data set. If x is a vector in a p
dimensional space and if p is small or correlations between these p variables are highly
related, then x does not need to be described by all the p variables. Fewer derived
variables (