Journal of Intelligent Learning Systems and Applications, 2011, 3, 45-54
doi:10.4236/jilsa.2011.31006 Published Online February 2011 (
Copyright © 2011 SciRes. JILSA
Apply GPCA to Motion Segmentation
Hongchuan Yu, Jian Jun Zhang
National Centre for Computer Animation, The Media School, Bournemouth University, Poole, UK.
Email: {hyu, jzhang}
Received October 14th, 2010; revised November 25th, 2010; accepted December 7th, 2010
In this paper, we present a motion segmentation approach based on the subspace segmentation technique, the genera-
lized PCA. By incorporating the cues from the neighborhood of intensity edges of images, motion segmentation is
solved under an algebra framework. Our main contribution is to propose a post-processing procedure, which can
detect the boundaries of motion layers and further determine the layer ordering. Test results on real imagery have con-
firmed the validity of our method.
Keywords: Ge ner al i zed PCA , Motion Segme nt ati o n, Layer Ordering
1. Introduction
An important problem in computer vision is to segment
moving objects of a scene from a video source, and part-
ly recover the structure or motion information, such as
foreground and background. With widespread demands
on video processing, motion segmentation has found
many direct applications. Video surveillance systems
seek to automatically identify people, objects, or activi-
ties of interest in a variety of environments with a set of
stationary cameras. Motion segmentation can provide
low level motion detection and region tracking cues.
Another relatively new application is markerless motion
capture for computer animation. It aims to estimate the
human body configuration and pose in the real world
from a video by locating the joint positions over time and
extracting the articulated structure.
Motion segmentation is expected to partly recover the
structure and motion information of moving objects from
a mutually occluded scene. This includes the following
main tasks, (1) labeling the regions of a motion layer
segmentation, i.e. pixels are assigned to several motion
layers; (2) finding their motion, e.g. each layer has its
own smooth flow field while discontinuities occur be-
tween layers; (3) determining the layer ordering, as the
different layers might occlude each other. But motion
segmentation is not equivalent to object tracking.
Roughly speaking, object tracking is to track the seg-
mented objects over an image sequence, although the
extension of the rigidity constraint to multiple frames is
nontrivial. Motion segmentation aims at the motion lay-
ers of a scene rather than the moving objects. For exam-
ple, if a moving object contains multiple motions at a
moment, it may be divided into several motion layers.
When these motion layers share the same motion, they
could be merged into a single layer. Hence, motion seg-
mentation usually uses the information from a few suc-
cessive frames. In contrast, object tracking focuses on a
moving object in a scene. It utilizes the information from
an image sequence. Motion segmentation plays a role of
fundamental module in motion analysis and tracking. [1]
presented a subspace segmentation method to estimate
the motion models of the motion layers based on two
successive frames. Built on this subspace segmentation
method, this paper will further aim at two other basic
problems of motion segmentation, i.e. the detection of
motion layer boundaries and depth ordering based on two
successive frames. The basic idea is to refine a global
segmentation to solve these two problems. We first ad-
dress this subspace segmentation approach for motion
model estimation. We then incorporate it with the inten-
sity edge information into a post-processing procedure,
which refines the layer boundaries and infers the layer
order between two successive frames. These two proce-
dures form a complete algorithm for motion segmenta-
tion. Our specific contributions in this paper include 1)
the Polysegment algorithm (a special case of the genera-
lized PCA [2]) is employed to detect the layer boundaries
in our post-processing procedure, and 2) the cues from
the intensity edges of images are utilized in the detection
of the layer boundaries and depth ordering.
Previous Works
Although motion segmentation has long been an active
Apply GPCA to Motion Segmentation
Copyright © 2011 SciRes. JILSA
area of research, many issues remain open in computer
vision, such as the layered motion descriptions [3,4],
occlusion detection and depth ordering [5-7], and estima-
tion of multiple motion models [8,9].
Most popular approaches to motion segmentation re-
volve around parsing the optical flow field in an image
sequence. Because of the well-known aperture problem,
the motion vector from optical flow computation can
only be determined in the direction of the local intensity
gradient. For the sake of completeness of optical flow
field, it is assumed that the motion is locally smooth.
Obviously, depth discontinuities and multiple indepen-
dently moving objects usually result in discontinuities of
the optical flow. The usual approaches are to parameter-
ize the optical flow field and fit a different model (e.g.
2D affine model) to each moving object, such as the
layered representation of the motion field [3]. The chal-
lenges of the optical flow-based techniques involve iden-
tifying motion layers (or pixel grouping), detecting layer
boundaries, and depth ordering. Previous research can
mostly be grouped into two categories. The first category
is to determine all of the motion models simultaneously.
This can be achieved by parameterising the motions and
segmentation, and using sophisticated statistical tech-
niques to predict the most probable solution. For example,
Smith et al. in [6] presented a layered motion segmenta-
tion approach under a Bayesian framework by tracking
edges between frames. In the implementation of their
proposed scheme, the region edge labels were not direct-
ly applied to the Bayesian model. They were implicitly
determined by the foreground-background orders of the
motion layers and the motion layer labels for each region.
Kumar et al. in [10] presented the learning approach of a
generative layered representation of a scene for motion
segmentation. In order to get the initial estimates of
model, they utilized the loopy belief propagation, and
further refined the initial estimate by using αβ-swap and
α-expansion algorithms. The large number of undeter-
mined parameters in their Bayesian models leads to the
difficult tracking problem in a high dimensional parame-
ter space. The second category is the dominant motion
approach [11-13]. A single motion is first fitted to all
pixels, and then to test for pixels that agree with that mo-
tion. This process can be repeated recursively on the out-
lier pixels to provide a full set of layers [12]. The central
problem faced by this kind of approaches is that it is ex-
tremely difficult to determine the occluded edges of the
moving regions (or motion layers). Furthermore, this
problem can result in the failure of depth ordering of
motion layers. However, analytically reasoning such
complex cases is impractical. The main reasons are three
fold. First, the smoothing required by the optical flow
algorithms makes it difficult to localize the layer boun-
daries. Second, the optical flow field is usually parame-
terized by some 2D motion models (e.g. 2D affine),
which is the first order approximation of the perspective
model. It is unreliable to apply a 2D model to the boun-
daries of moving regions. Third, pixels in a neighbor-
hood of the boundaries are in the areas of high intensity
gradient. Slight errors or image noise can result in pixels
of a very different intensity, even under the correct mo-
tion estimate [6]. In this paper, we will simplify the
problem of motion segmentation based on an algebraic
framework. We will first obtain a rough global segmen-
tation and then refine it afterwards.
Our work is partially inspired by the subspace seg-
mentation approach to motion model estimation pro-
posed in [1]. This approach can provide a non-iterative
and global estimation of motion layer segmentation. But
it is incomplete, since the depth ordering and the detec-
tion of layer boundaries are ignored. In this paper we
provide a complete solution by developing a novel post-
processing procedure using the intensity structures of
edges for the detection of (1) motion layer boundaries
and (2) the layer order.
In the remainder of this paper, we first briefly review
the subspace segmentation approach to motion model
estimation [1] in section 2. In section 3, a post-processing
procedure is presented for the detection of the layer
boundaries and depth ordering. The experimental results
and analysis are given in section 4. Our conclusion and
future work are given in section 5.
2. Motion Segmentation by GPCA-PDA
The core of our proposed motion segmentation approach
is the scheme of segmenting hyperplanes in
R, which
is called the generalized PCA (GPCA) in [2]. Applying
the GPCA method to motion model estimation has been
proposed in [1]. But the resulting motion model estima-
tion can only yield coarse motion segmentation, i.e. the
boundary of the motion layers is very blurry. Our basic
idea is to further refine the boundary of the resulting mo-
tion layers by a post-processing procedure. Before intro-
ducing our post-processing procedure, we firstly review
the motion model estimation approach in [1] briefly. The
two used algorithms, GPCA-PDA Alg. and Polysegment
Alg., can be found in [2]. (We also briefly introduce
these two algorithms in Appendix.)
The first problem to motion segmentation is to obtain
the layered motion models corresponding to indepen-
dently moving regions in a scene, (i.e. layer segmenta-
tion). We address an algebra approach in terms of a
known optical flow field which has been presented in [1].
Its distinct advantage over the other approaches is that it
can determine all motion layers simultaneously.
Given N measurements of the optical flow
Apply GPCA to Motion Segmentation
Copyright © 2011 SciRes. JILSA
uv at the N pixels
12 1
xx , we can de-
scribe them through a affine motion as follows,
11 112213
21 122223
ax axau
ax axav
In terms of the hyperplane representation in the Ap-
pendix, the solution to the multiple independent affine
models can be rephrased as follows. Let
xx uvR and hyperplane i
S be spanned
by the basis of
baaaa and
,,,0, T
baaaa. We need to segment a mixture
of the hyperplanes of dimension 3d in 5
R, which is
expressed as,
:, 0
SxRbbx .
The original equations of optical flow have finished
the projection from 5
R to two individual subspaces
of 4
R in a natural way, i.e. each new hyperplane in 4
can be expressed as,
11 1213141234
,,,,,, 0aaaa xxxx.
Applying the scheme of Equations (A1)-(A4) in Ap-
pendix can yield the desired basis
Bbb for
each hyperplane i
S in 3
Up to now, one can obtain the initial estimation of all
of the motion layers simultaneously. This is insufficient
for motion segmentation, since we also need to determine
the layer boundaries and the occlusion relationship. Be-
side that, it can be observed that each segmented layer
contains some small and isolated spurious regions, and
the resulting layer boundaries wander around the real
ones. This makes the detection of the layer boundaries
difficult. The occluded regions take place in the neigh-
borhood of the layer boundaries. If the occluding edges
can be determined correctly, the occluded regions can be
segmented correctly. Furthermore, the resulting motion
layers can also be linked to the occluded regions in terms
of the occluding edges for the depth ordering. Hence, it is
a crucial step to determine the occluding edges. Our de-
velopment is based on the following observations (1) the
intensity edges include the boundaries of motion layers;
(2) the layer boundaries are not always the occluding
edges; (3) determining the occluding edges and inferring
the occlusion relationship can be fulfilled by testing the
neighborhood of edges.
We will introduce the intensity edges of images into
the potential occlusion areas for the detection of occlud-
ing edges in the next section.
3. Post-Processing Procedure
Let us consider a single viewpoint. The central problem
is to detect the occluding edges, because the erroneous
edge labeling can cause incorrect depth ordering. Most of
the techniques considered so far employed only the mo-
tion field information for motion segmentation. For each
frame, all edges, including edges of motion layers and
textured edges of objects, are presented in the image in-
tensity structure, which can provide the wealth of addi-
tional information to motion estimation. Due to their ex-
treme length, a number of measurements might be taken
along (or around) them. This leads to a more accurate
estimation of motion.
Recent applications have motivated a renewal of mo-
tion segmentation by tracking edges [6,14]. Ogale et al.
[7] classified the occlusions into three classes. In order to
deduce the ordinal depth, they had to fill the occluded
regions. This is to implicitly approximate the occluding
edges by filling the neighborhood of the layer boundaries.
[6] provides three fundamental assumptions of the rela-
tionship between regions and edges to identify the edges
of moving regions. We add an extra assumption (i.e. the
4th below) and emphasize these four assumptions as fol-
1) As an object moves all of the edges associated with
that object move, with a motion, which may be approx-
imately described by some motion model.
2) The motions are layered, i.e. one motion takes place
completely in front of another, and the layers are strictly
ordered. Typically the layer farthest from the camera is
referred to as the background, with nearer foreground
layers in front of this.
3)An arbitrary segmented image region only belongs
to one motion model, and hence any occluding boundary
is visible as a region edge in the image.
4) For each frame, the intensity edges involve the
edges of motion layers.
An important conclusion from these four assumptions
is that the layer ordering can be uniquely determined if
the layer of each moving region is known and the oc-
cluding edges are known. [7] presented the relationship
of motion layers and occluded regions, and further em-
phasized that the motion layer involving the occluded
region must be behind another one. Even when the layers
of motion regions are known, ambiguities may still be
presented in the layer boundary labeling, as shown in
Figure 1. In Figure 1(a), due to the occluded region C,
we can infer the occlusion relationship between the mo-
tion regions A and B, while, in Figure 1(b), we cannot
find out the layer order according to the distinct edges of
the motion layers. The layer boundaries are not the same
as the occluding edges. The layer boundaries involve the
occluding edges, but the layer boundaries are not always
the occluding edges. It is infeasible to infer the layer or-
der only by the layer boundaries. We can therefore con-
Apply GPCA to Motion Segmentation
Copyright © 2011 SciRes. JILSA
(a) (b)
Figure 1. Illustration of moving re gions (A,B) and occluded regi on (C). (a) The probable layer boundar ies are determined by
extending the moving region to the occluded region; (b) ther e is no occlusion r e g ion be tween layers A and B.
clude that the occlusion relationship hides behind the
occluded regions, and identifying the occluding edges
can reveal the occlusion relationship. The optical flow
computation can usually identify the coarse occlusion
regions as a by-product [15], which will be adopted in
this paper.
The subspace segmentation approach described in sec-
tion 2 is carried out on a given optical flow field instead
of the image intensities. Due to the errors from the opti-
cal field (e.g. aperture problem etc.), each resulting mo-
tion layer contains two kinds of artifacts: (1) small iso-
lated regions with texture and (2) dark holes over the
image plane. It can be observed that a single hole in the
middle of a foreground layer runs through to the back-
ground layer. Similar problems also exist in the occluded
regions. Moreover, the resulting boundaries of motion
layers and their neighborhood are, in general, highly un-
reliable areas. Therefore, the segmentation by the sub-
space segmentation method and the occluded regions
detected by the optical flow computation cannot offer a
valid solution to the above two problems.
Consider the neighborhood of the layer boundaries. It
can be observed that the occluded regions are involved in
the neighborhood of the layer boundaries as shown in
Figure 1(a). The edges’ neighborhood contains the
wealthy intensity structures of image. This can provide
us sufficient cues to find the layer boundaries and oc-
cluding edges. We rephrase the problem of layer edge
detection and depth ordering, and present our postpro-
cessing procedure as follows.
The motion models of the layers are determined by the
subspace segmentation approach described in section 2,
while the layer boundaries and the layers of the occluded
regions are undetermined. The problem we face here is
how to determine the layer boundaries and infer the oc-
clusion relationships. In order to do that, we will consider
the intensity structures of each frame, the relevant occlu-
sion region map (obtained by [15]) and the relevant
boundary map of the initial motion layers (obtained by
the subspace segmentation approach). Let us denote in-
tensity edge map as
, occlusion region map as O
and layer edge map as
thereafter. The motion of
intensity edges dominates that of their neighborhood. It is
straightforward to utilize the intensity structures of the
neighborhood of the edges for detecting the layer boun-
daries and inferring the occlusion relationship. The pro-
posed post-processing procedure given below is per-
formed over two successive frames, but evidence could
be accumulated over an image sequence for a more ro-
bust segmentation.
3.1. Construct Pending Areas
For each frame, we first determine some pending areas,
which should involve all potential layer boundaries. Then,
the detection of layer boundaries is carried out on the
resulting pending areas accordingly. To this end, we
place a set of windows w of size .nn
along the edges
These small windows might be overlapped to
each other. Usually each window i
w is determined by
the O
without a fixed size, i.e. it is ex-
pected to be so large that the resulting set of windows
can cover the occlusion regions O
and layer edge
on the current frame. In our experiments, the
minimal size n of i
w is set to 10 pixels.
3.2. Match Scores
Consider the resulting pending areas i
Ww, which
contains many intensity edges
lM. The potential
layer boundaries are involved in .
in terms of the
assumption (4). Thus, for each window i
w, we can
compute the profile of every point p, which is defined as
a vector
pfp by sampling the intensity derivative in
the positive and negative directions of the intensity gra-
dient at p. This is illustrated in Figure 2. The point pro-
file is then normalized as,
 
pf p
pf ppf p
. (1)
According to the optical flow field, one can get a pair
of corresponding points p and p respectively on two
Possible layer boundaries Layer boundary
Apply GPCA to Motion Segmentation
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frame 1 frame 2
Normal line
Refining & match score
Match scores
Figure 2. Illustration of point profiles and refining the
matching point. Refining procedure is carried out as 1D
search along the normal line on the frame 2.
successive frames. The match score is taken as the resi-
dual error of their profiles as follows,
 
pfp pfp
ep Exp
is a reference distance and is determined em-
pirically. When the point p is far away from layer
should approach one, i.e. the
neighborhood of p obeys a single motion. Otherwise its
neighborhood contains multiple motions. Furthermore,
we can obtain other two match scores respectively along
either side profile of the current point p, denoted as
 
. If point p belongs to a layer boundary, one
of these two scores should approach one while the other
should approach zero, otherwise both of them should
approach one.
3.3. Matching
Because of the aperture problem, the motion of edges can
only be determined in the direction normal to the edge.
This means that the corresponding point p of the next
frame (1i) lies on the normal line, which is the normal
at point p on the current frame (i). This is useful as it
restricts matching isointensity contour on the frame (1i
along the edge normal. In order to enhance the intensity
edge matching, we add a new match score that is the re-
sidual error
of the profiles of p and p
along the edge tangent line, which is shown in Figure 2.
Refining the matching point p
on the next frame (1i
is thus implemented as a 1D search based on the match
score of
 
epe p
along the direction of point
p’s gradient (i.e. the normal line) instead of point p
gradient, which is also illustrated in Figure 2. After that,
one can re-compute the match scores 012
,,eee of the
points p and p in terms of their individual intensity
gradients rather than the normal line.
3.4. Segmentation by Polysegment Alg.
Based on the match scores 012
,,eee in the pending areas
W, we apply the Polysegment algorithm as described in
the Appendix respectively to the match scores of
,,eee for the layer edge detection. There are two
groups here, one is the group of layer edge points and the
other is that of non-layer edge points. For each match
score, we can thus get two cluster centers
()() ()
. Moreover, to the points
, there are eight cluster centers. The layer edge
points should cluster around the two centers of
(0) (1)(2)
1min,min, maxcent
(0)(1) (2)
2min,max, mincent
The segmentation of W is obtained as follows,
1, ,8
arg minj
ie pcent
, (2)
,,epeee p. On this basis, the points of
W can be classified into two groups, layer edge points
and non-layer edge points.
3.5. Region Merging
For the group of non-layer edge points, one can merge
most of small spurious regions in a big motion layer, i.e.
merging small regions with a motion layer by comparing
their areas with their individual neighbors’. This can lead
to the connected layer. But it can be observed that the
detected layer boundaries usually have discontinuities
with the group of layer edge points, i.e. a set of layer
edge segments. This is due to the fact that some layer
edge points are incorrectly classified into the group of
non-layer edge points. Based on the areas of the seg-
mented layer regions, it is impossible to make a correct
decision of region merging when these small regions
may contain layer edge segments. This is because the
layer edge segments indicate that the both sides should
respectively occupy different motion layers and could not
be merged into a single layer at anytime. These regions
are thus left as the undetermined regions temporarily.
On the other hand, a connected layer has a continuous
. These layer edge segments only prune
the region of the layer, but do not form new closed re-
gions within the layer. In our experiments, we simply
replace some parts of
with the new layer edge
segments according to the nearest neighbor criterion.
Then, the area comparison strategy is employed to those
undetermined regions nearby the layer boundary for re-
gion merging. A layer edge segment separates one region
into two motion layers. When merging two or more un-
determined regions which share a layer edge segment,
Apply GPCA to Motion Segmentation
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the merging procedure should be terminated.
3.6. Depth Ordering
After region merging, one can obtain the desired bounda-
ries of motion layers
. If the occluded regions be-
long to a motion layer, this layer must be behind another
one. Our problem can now be rephrased as HOW to as-
sign the occluded regions to the known motion layers.
With the occlusion region map O
, one can first de-
termine which points of the layer boundaries belong to
the occluding edges, since the layer boundaries involve
the occluding edges. The worst case is that the points of
the occluding edges are not within O
. But both side
profiles of these points should overlap with O
at that
moment. On this basis, one can determine the points of
the occluding edges by checking if they are within O
or their profiles overlap with O
. Since some points of
layer boundaries may not belong to the occluding edges,
such as in Figure 1(b), the depth ordering can only carry
out on the detected occluding edges. Then, for the points
of the occluding edges, one can extend their profiles in
the direction of the intensity gradient to the known mo-
tion layers for their profile labeling, i.e. inferring which
layers both sides of the occluding edges respectively be-
long to.
Furthermore, inferring the occlusion relationship can
be fulfilled by comparing the match scores
 
,epep of each point p of the occluding edges.
This is because an occluded region only shares the same
motion layer with one of the profiles of an occluding
edge. The smaller match score corresponds to the real
occlusion region. This implies that one side of an oc-
cluding edge with the smaller match score is behind the
other side, since it involves the occlusion region. In terms
of the profile labeling of the occluding edge points, we
can therefore find ordinal depth.
The Post-processing procedure is summarized as
1) Extracting the pending areas W on each frame;
2) Refining the corresponding points p on the next
frame (1i), and then re-compute the match scores
 
3) Applying the polysegment algorithm to the match
scores of W for detecting the points of layer boundaries;
4) Merging the spurious regions for the continuous
boundaries of the motion layers;
5) Determining the occluding edges in terms of O
6) Extending the profiles of the occluding edge points
to the known motion layers for the profile labeling;
7) Comparing the match scores
 
,epep of the
occluding edge points p for depth ordering, i.e.
 
min ,epep corresponds to the occluded region.
This post-processing procedure and the subspace seg-
mentation approach described in section 2 constitute a
complete algorithm of motion segmentation. Note that in
our algorithm, the estimation of all the motion models in
a scene is undertaken at the first procedure (i.e. subspace
segmentation method), and the detection of the layer
boundaries and depth ordering are carried out at the
second procedure (i.e. post-processing procedure). This
is different from the previous approaches. Usually the
motion model estimation was mixed with the later
processing. This makes the algorithms complicated and
the implementation difficult.
4. Experiments and Analysis
Our algorithm was tested on several image sequences. In
this section, the two results of the “flower garden” and
“Susie calling” are presented. All programs have been
implemented on the MatLab platform using a publicly
available package—GPCA-PDA [16] and the optical
flow code in [15]. All the image sequences used in our
experiments are available at [17].
4.1. Flower Garden
In this experiment, we applied our motion segmentation
approach to the flower garden sequence of resolution
175×120 pixels. The tree trunk in front of a garden is
taken by a camera undergoing translation from the left to
the right. Our goal is to determine the boundaries of the
motion layers, and find the layer order over two succes-
sive frames. Our approach found out two motion layers,
the tree trunk and garden background.
Figure 3 gives the segmentation of the affine model
using the subspace segmentation approach described in
section 2. It can be noted that the occlusion regions from
the optical flow fields are crude, and contain many spu-
rious small regions. The red arrows illustrate the possible
occlusion regions in the successive frame 1 and 2. We
show the results of motion segmentation of frame 1 in
Figure 3(1)-(3). Note that the occlusion regions in Fig-
ure 3(2) and (3), which are not the layer boundaries, are
the interim areas between the foreground and background.
It is impossible to determine the depth ordering using the
obtained motion layers before determining the layers of
the occluded regions. In addition, the resulting layer
boundaries are also unreliable. Similar to the occluded
regions, there are many small and isolated spurious re-
gions on the obtained layers. We need to refine the layer
boundaries and find out the layer order.
Figure 4 gives the segmentation results of the sub-
space segmentation approach followed by the post-
processing procedure described in section 3. The boun-
daries of motion layers can go across the occluded re-
gions and converge to the desired locations. But we can
also note that a patch of ground is classified as the fore-
Apply GPCA to Motion Segmentation
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frame 1 frame 2
(1). Occlusion regions (2). (3).
Figure 3. Motion segmentation results by the subspace segmentation approach only. (1) The occlusion regions from the opti-
cal flow field betw een frame 1 and 2; (2) and (3) are the segmentation results only using the subspace segmentation approach
described in section 2.
(1). Foreground (2). Background
(3). Intensity edge map (4). Occluding edges (red)
Figure 4. Refined and layered motion segmentation. (1) and (2) are the segmentation results; (3) the intensity edges of the
image; (4) occluded region and occluding edges.
ground as shown in Figure 4(1). This is due to the fact
that the motion variance of this patch between the suc-
cessive frames 1 and 2 is close to that of the “tree”, away
from the background. If going through over multiple
frames, the motion of this patch should be distinguished
from the “tree”, since the motion of the ground is prone
to be modeled by a single affine model. The intensity
edge map of frame 1 is obtained by the Canny edge de-
tector, and also shown in Figure 4(3). It can be observed
that the boundaries of motion layers are involved in the
intensity edge map, e.g. the red arrows illustrate the cor-
responding edges between the layer boundaries and the
intensity edges of the image. Moreover, we also show the
occlusion edges (red) in Figure 4(4). Partial occluding
edges are not involved in the initial occlusion regions.
But, the profiles of these edge points overlap with the
occlusion regions. These points can thus be joined with
the occluding edges. Additionally, it can be noted that the
layer boundaries involve the occluding edges, but the
layer boundaries are not always the occluding edges.
Locating the occluding edges can help us find the depth
4.2. Susie Calling
This sequence presents a hand holding a phone while the
head is rising slightly. The image resolution is 170120
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frame 1 frame 2 (1). Background (2). Phone regions
(3). Head region (4). Background (5). Phone regions (6). Head region
(7). Occluded region
Figure 5. The segmentation of the Susie calling. (1)-(3) are the results of the subspace segmentation method; (4)-(6) are the
results of the post-processing proc e dur e; (7) the layer boundary goes thro ugh the occluded regions.
pixels. It can be observed that the region of the phone is
enwrapped by the head region. Our segmentation appro-
ach aims at separating the phone region from the head
region. The segmentation results are shown in Figure 5.
The region of phone is in front of the regions of head and
background. The background region is behind the head
Due to the rich texture of the hair, the segmented head
region contains many small holes, particularly in the hair
area. It is difficult to determine the boundaries of the hair.
For example, in Figure 5(2), a patch of hair image is
incorrectly classified into the group of the phone region.
The post-processing could not merge this patch into the
head region either, as shown in Figure 5(4)-(6). This is
because the detected layer edge segments goes through
the occluded regions as shown in Figure 5(7). Moreover,
it has been judged that the regions of the phone and the
hair patch are in front of the head region. This seems to
be a bit strange. In general, the hair patch belongs to the
head region. All the hair should be regarded as a whole
body on the head and there is no occlusion to each other
(unless the hairlines are considered). But it can be ob-
served that the occluded regions overlying on the layer
boundary appear at the bottom right of the image, i.e.
around the boundaries between the shoulder and the hair.
The motion of the hair is independent of that of the
shoulder. The shoulder region is classified into the head
region. Thus, it is acceptable to preserve this patch as an
independent layer as shown in Figure 5(5).
5. Conclusions
In this paper, we proposed a novel approach for motion
segmentation based on the subspace segmentation tech-
niques. The novelty is that by incorporating the intensity
structures of images, our proposed approach can effec-
tively detect the motion layer boundaries and the depth
ordering. Different from the previous motion segmenta-
tion approaches, our approach provides a non-iterative
and global solution to motion segmentation under a uni-
fied algebra framework, i.e. the generalized PCA [2,18].
However, it can be noted that our algorithm relies on a
given optical flow field. In our experiments, many avail-
able optical flow algorithms do not seem suitable for the
scenarios with a salient rotation element. This will re-
strict the applications of our algorithm. It is crucial to
further develop a robust optical flow algorithm. Our fu-
ture work will aim to tackle this challenge.
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codes available at
[17] Video sequences available at
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Segmenting Hyperplanes of Dimension K–1 in
Given a set of points
Xx R
 in a homoge-
neous coordinate system, and linear hyperplanes
of dimension
dim 1
kSK, we
need to identify i
S. Usually the subspace is given as,
() ()
bx b R . Then, this hyperplane can be re-
presented as,
SxRxb .
Furthermore, an arbitrary point x lies on one of the
hyperplanes if and only if,
xSxS xb
px xb
00 11000
121 211
,,, T
nn nm
yxxxxxx xxxR
 and
cR is a coefficient vector consisting of a set of
monomials of ()
b and
. For a
given point set
, we have a linear system on n
c as
nn n
Lcc R
. When the number of hyperplanes n
is known, n
c can be obtained from the null space of
L. In practice, n is always determined in terms of n
For a unique solution of the coefficient vector n
c, it is
expected that
, which is a func-
Apply GPCA to Motion Segmentation
Copyright © 2011 SciRes. JILSA
tion of variable n. In the presence of noise, let
rank Lr when ni and 1
, where
L is the data matrix with rank r,
is the jth sin-
gular value of i
L and is a given threshold.
For any
, we have
 
px cyx. Each normal
vector ()i
b can be obtained from the derivatives of n
Consider the derivative of
px as follows
 
()()( )
nTiiT j
pxxbb xb
 
For a point ()ll
S, () ()0
jT l
for li. It can
be noted that there is only one non-zero term in
()()( )()0
pxbb x
 
for li. Then, the
normal vector of i
S is yielded as,
In order to get a set of points lying on each hyperplane
respectively, so as to determine the corresponding nor-
mal vectors ()i
b, we can choose a point in the given X
close to one of the hyperplanes as follows,
arg min
px n
, where
X (A3)
After given the normal vectors
b, we can classify
the whole point set X into n hyperplanes in
R as fol-
arg min,1
labelx bjn (A4)
This algorithm is called GPCA-PDA Alg. in [2,18].
Polynomial Segmentation Algorithm
Consider a special case of piecewise constant data. Given
N data points
, we hope to segment them into an
unknown number of groups n. This implies that there
exist n unknown cluster centers 1n
, so that,
 ,
which can be described in a polynomial form as follows,
px xcx
 
To N data points, we have,
Lc c
where (1)Nn
and 1n
. Usually, the group
number is estimated as,
min: ii
where i
is the ith singular value of i
L, which is the
collection of the first 1i
columns of n
L, and is a
given threshold that depends on the noise level.
After solving the coefficient vector c of Equation (A6),
we can compute the n roots of
px, which corres-
pond to the n cluster centers 1
. Finally, the seg-
mentation of the date is obtained by,
1, ,
arg minj
The scheme of Equations (A5)-(A8) is called as the
Polysegment algorithm in [18].