Research
Topics
The
recognition task with a model image has
received increasing attention for the
important applications
such as automatic passport control at
airport, where a single photo in
the passport is available as a model, and
face image retrieval in Internet/or
unknown database.
In the retrieval task, a single arbitrary
query image is supplied by users and
every
single image in the unknown database is
matched with the single query, bringing a
Single-to-Single
matching problem. The task has emerged as
an active research area in FRT (Face
Recognition Test) and MPEG-7 (Moving
Picture Experts Group) Standardisation
for
face image retrieval. The
example patterns are shown in Figure 1.
Selected
Publication: T-K.
Kim and J. Kittler, Locally Linear
Discriminant Analysis for Multi-modally
Distributed Classes for Face Recognition
with a Single Model Image, IEEE Trans.
on PAMI, 27(3):318-327, 2005 (Regular
paper).
(a)
Single-to-Single matching strategy
(b)
Single face images captured at different
views (XM2VTS).
Figure
1. Example Patterns For
Single-to-Single Matching. Given
a single model image per a
subject (for e.g. a frontal view), novel
view images should be classified.
Rather
than a single image input, more robust
object recognition can be achieved classifying
a set
of images which represents a variation
in object’s appearance. Sets may be
derived from
sparse and unordered observations acquired
by multiple still shots as well as
from a video sequence. That is, the
objective of this task is to classify an
unknown set of images
to one of the training classes, each of
which is also represented by images sets.
The example
sets of an isolated general object are
shown in Figure 2.
Selected
Publication: T-K.
Kim, J. Kittler and R. Cipolla,
Discriminative Learning and Recognition of
Image Set Classes Using Canonical
Correlations, IEEE Trans. on PAMI,
29(6), 2007 (Regular paper).
(a)
Set-to-Set matching strategy
(b)
A set of object images collected from a
turn-table sequence.
(c)
A set of object images collected from a
random-moving-camera sequence.
Figure
2. Examples Patterns For Set-to-Set
Matching. The
two sets contain different pattern variations
caused by different views and lighting.
Over
the last decades, human action/gesture
classification has been one of the most
important
topic in computer vision for a variety of
tasks such as video surveillance,
object level video
summarization, video indexing, digital
library organization, etc. This
task can be
tackled by classifying spatiotemporal
patterns in aligned videos. Action
detection may
be first performed out to localize unit
actions in input videos. Example video
samples with
indication of action alignments are shown
in Figure 3 (Also see our own hand-gesture
data set).
Selected
Publication:
T-K. Kim,
S-F. Wong and R. Cipolla, Tensor Canonical
Correlation Analysis for Action
Classification, In Proc. of IEEE
Conference on CVPR, Minneapolis, MN,
2007.
S-F. Wong, T-K. Kim and R. Cipolla,
Learning Motion Categories Using Both
Semantic and Structural Information, In
Proc. of IEEE Conference on CVPR,
Minneapolis, MN, 2007.
(a)
Video-to-Video matching strategy
(b)
Example action classes (KTH). The bounding
box and superimposition indicate the action
alignment in a spatiotemporal space.
Figure
3. Examples Patterns For
Video-to-Video Matching.
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