Motion rank: applying page rank to motion data search
As the uses of motion capture data increase, the amount of
available motion data on the web also grows. In this paper, we
investigate a new method to retrieve and visualize motion data in
a similar manner to Google Image Search. The main idea is to
represent raw motion data into a series of short animated clip
arts, called motion clip arts. Short animated clip arts can be
quickly browsed and understood by people even though many of them
appear at the same time on the screen. We first temporally
segment the raw motion data files into short yet semantically
meaningful motion segments. Then, we convert the motion segments
into motion clip arts in a way that emphasizes the main motion
and minimizes the data size for the efficient transmitting and
processing on the web. When a user input query is received, our
system first retrieves all the relevant motion clip arts by
considering the input keywords and similarity between motions.
Then, the retrieved results are re-ranked by our ranking
algorithm developed based on the Google ImageRank algorithm. To
prove the usability of our method, we build a web-based motion
search system with the entire data collections of the CMU motion
database. The experimental results show significant improvement,
in terms of relevancy, in comparison with the simple
keyword-based search interface.
Myung Geol Choi, The Catholic University of Korea.
Taesoo Kwon, Hanyang University.
Myung Geol Choi, Taesoo Kwon, Motion rank: applying page rank to motion data search.
The Visual Computer, Volume 35, Issue 2, pages 289-300, February 2019.