12 research outputs found
Exact Mean Computation in Dynamic Time Warping Spaces
Dynamic time warping constitutes a major tool for analyzing time series. In
particular, computing a mean series of a given sample of series in dynamic time
warping spaces (by minimizing the Fr\'echet function) is a challenging
computational problem, so far solved by several heuristic and inexact
strategies. We spot some inaccuracies in the literature on exact mean
computation in dynamic time warping spaces. Our contributions comprise an exact
dynamic program computing a mean (useful for benchmarking and evaluating known
heuristics). Based on this dynamic program, we empirically study properties
like uniqueness and length of a mean. Moreover, experimental evaluations reveal
substantial deficits of state-of-the-art heuristics in terms of their output
quality. We also give an exact polynomial-time algorithm for the special case
of binary time series
Making the Dynamic Time Warping Distance Warping-Invariant
The literature postulates that the dynamic time warping (dtw) distance can
cope with temporal variations but stores and processes time series in a form as
if the dtw-distance cannot cope with such variations. To address this
inconsistency, we first show that the dtw-distance is not warping-invariant.
The lack of warping-invariance contributes to the inconsistency mentioned above
and to a strange behavior. To eliminate these peculiarities, we convert the
dtw-distance to a warping-invariant semi-metric, called time-warp-invariant
(twi) distance. Empirical results suggest that the error rates of the twi and
dtw nearest-neighbor classifier are practically equivalent in a Bayesian sense.
However, the twi-distance requires less storage and computation time than the
dtw-distance for a broad range of problems. These results challenge the current
practice of applying the dtw-distance in nearest-neighbor classification and
suggest the proposed twi-distance as a more efficient and consistent option.Comment: arXiv admin note: substantial text overlap with arXiv:1808.0996
A 32 sub-band/transform coder incorporating vector quantizing for adaptive bit allocation
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1983.MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERINGVita.Includes bibliographical references.by Courtney D. Heron.M.S
Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition
Recently, several clustering algorithms have been used to solve variety of
problems from different discipline. This dissertation aims to address different
challenging tasks in computer vision and pattern recognition by casting the
problems as a clustering problem. We proposed novel approaches to solve
multi-target tracking, visual geo-localization and outlier detection problems
using a unified underlining clustering framework, i.e., dominant set clustering
and its extensions, and presented a superior result over several
state-of-the-art approaches.Comment: doctoral dissertatio