A new heuristic algorithm for fast k-segmentation
The $k$-segmentation of a video stream is used to partition it into $k$ piecewise-linear segments, so that each linear segment has a meaningful interpretation. Such segmentation may be used to summarize large videos using a small set of images, to identify anomalies within segments and change points...
Saved in:
Main Authors | , |
---|---|
Format | Journal Article |
Language | English |
Published |
02.09.2020
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | The $k$-segmentation of a video stream is used to partition it into $k$
piecewise-linear segments, so that each linear segment has a meaningful
interpretation. Such segmentation may be used to summarize large videos using a
small set of images, to identify anomalies within segments and change points
between segments, and to select critical subsets for training machine learning
models. Exact and approximate segmentation methods for $k$-segmentation exist
in the literature. Each of these algorithms occupies a different spot in the
trade-off between computational complexity and accuracy. A novel heuristic
algorithm is proposed in this paper to improve upon existing methods. It is
empirically found to provide accuracies competitive with exact methods at a
fraction of the computational expense.
The new algorithm is inspired by Lloyd's algorithm for K-Means and Lloyd-Max
algorithm for scalar quantization, and is called the LM algorithm for
convenience. It works by iteratively minimizing a cost function from any given
initialisation; the commonly used $L_2$ cost is chosen in this paper. While the
greedy minimization makes the algorithm sensitive to initialisation, the
ability to converge from any initial guess to a local optimum allows the
algorithm to be integrated into other existing algorithms. Three variants of
the algorithm are tested over a large number of synthetic datasets, one being a
standalone LM implementation, and two others that combine with existing
algorithms. One of the latter two -- LM-enhanced-Bottom-Up segmentation -- is
found to have the best accuracy and the lowest computational complexity among
all algorithms. This variant of LM can provide $k$-segmentations over data sets
with up to a million image frames within several seconds. |
---|---|
DOI: | 10.48550/arxiv.2009.05148 |