The task of multi-body motion segmentation refers to segmenting feature trajectories
in a sequence of images according to their 3D motion affinity without
knowing the number of motions in advance. It is critical for understanding and
reconstructing a dynamic scene. This problem essentially consists of two subproblems,
segmenting features and detecting the number of motions. While the
state-of-the-art LBF algorithm achieves segmentation accuracy as high as 96.5%,
it is still disturbed by a phenomenon called over-locality. A novel mode seeking
algorithm with an adaptive distance measure is proposed to avoid this problem,
and improves the accuracy to 98.1%. The LBF algorithm is incapable of detecting
the number of motions itself. A randomized version of the mode seeking algorithm
is presented, which could detect the number as well as preserve satisfactory
segmentation accuracy. To detect the number of motions, a kernel optimization
method locates it via kernel alignment. However, it suffers from over-locality and
over-detects the number of motions. An intersection measure and two mutual
information measures are presented to solve this problem. Using these measures,
the proposed clustering consensus framework recasts the motion number detection
problem to a clustering consensus problem. It extends the kernel optimization
method from two-clustering consensus to multiple-clustering consensus. A large
number of experiments and comparisons have been done, and convincing results
are obtained. / published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy
| Identifer | oai:union.ndltd.org:HKU/oai:hub.hku.hk:10722/167212 |
| Date | January 2012 |
| Creators | Pan, Guodong., 潘国栋. |
| Contributors | Wong, KKY |
| Publisher | The University of Hong Kong (Pokfulam, Hong Kong) |
| Source Sets | Hong Kong University Theses |
| Language | English |
| Detected Language | English |
| Type | PG_Thesis |
| Source | http://hub.hku.hk/bib/B48199369 |
| Rights | The author retains all proprietary rights, (such as patent rights) and the right to use in future works., Creative Commons: Attribution 3.0 Hong Kong License |
| Relation | HKU Theses Online (HKUTO) |
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