=Paper=
{{Paper
|id=Vol-1366/paper5.pdf
|storemode=property
|title=Automated Silhouette Extraction for Mountain Recognition
|pdfUrl=https://ceur-ws.org/Vol-1366/paper5.pdf
|volume=Vol-1366
|dblpUrl=https://dblp.org/rec/conf/gvd/BraunS15
}}
==Automated Silhouette Extraction for Mountain Recognition==
https://ceur-ws.org/Vol-1366/paper5.pdf
Automated Silhouette Extraction for Mountain Recognition
Daniel Braun Michael Singhof
Heinrich-Heine-Universität Heinrich-Heine-Universität
Institut für Informatik Institut für Informatik
Universitätsstraße 1 Universitätsstraße 1
40225 Düsseldorf, Deutschland 40225 Düsseldorf, Deutschland
braun@cs.uni-duesseldorf.de singhof@cs.uni-duesseldorf.de
ABSTRACT 1. INTRODUCTION
With the rise of digital photography and easy sharing of Sharing our experiences with digital images is a significant
images over the internet, a huge amount of images with no part of our today’s life, which is partly a result of the high
notion of what they are showing exists. In order to overcome availability of digital cameras, like in smartphones, and the
this problem, we – for the example of mountain recognition – high use of social networks, that simplifies the publication
introduce a method, that is able to automatically recognise and sharing of images. As a consequence, the number of
a mountain shown in a photography. images in the world wide web increases significantly. For
Our method does not require GPS information stored in example, this can be seen on the image sharing platform
the image, since most images are not GPS tagged, either Instagram, where users share an average of 70 million new
because of the absence of a GPS sensor in the device or photos per day [1].
because it has been deactivated for a lesser power consump- As a result of such high numbers of images, searching
tion, which often is the case with smartphones. Instead, we photos which show specific objects is challenging, because
propose a method that is able to automatically extract the the majority of these images is not properly tagged with the
mountain’s silhouette from a given image. This silhouette names of every object seen in them. So the need for efficient
is then cleaned by removing artefacts and outliers, such as algorithms for automatic object recognition rises. In the last
trees and clouds, with a histogram based approach. Finally, decades there were many advances in this research field, but
the cleaned silhouette can be compared to reference data in especially the automatic identification of landmarks, which
order to recognise the mountain that is shown in the pic- are subject to weather changes, areal erosion and vegetation,
ture. For this, time series comparison techniques can be is still a challenging task, even if the amount of images with
used to find matching silhouettes. However, because of the attached GPS data, which marks the geo-position of the
huge number of reference silhouettes to compare against, we camera when the photo was shot, is rising.
argue, that a preselection of those silhouettes is necessary The growing spread of devices with the capability of gen-
and point out approaches to this problem. erating GPS tags for the images, like smartphones and dig-
ital cameras with GPS units, enables many possibilities for
Categories and Subject Descriptors an subsequent geo-localisation of images, due to the fact
that GPS tags can significantly reduce the number of pos-
I.4.8 [IMAGE PROCESSING AND COMPUTER VI- sible sights, buildings or landmarks to compare with. How-
SION]: Scene Analysis—Object recognition; I.4.6 [IMAGE ever, there exist too many images without the advantage
PROCESSING AND COMPUTER VISION]: Seg- of GPS tags, so that an automatic geo-localisation without
mentation—Edge and feature detection; H.2.8 [DATABASE prior knowledge of the camera position is still a valuable
MANAGEMENT]: Database Applications —Data min- aim.
ing Our focus lies on the automatic landmark recognition,
which we will describe by using the example of mountain
Keywords recognition in images. To solve the question, which moun-
tain can be seen on an given image, we match the skyline
Object Recognition, Image Annotation, Outlier Detection,
of the mountain in the image with silhouettes of mountains
Image Segmentation, Skyline Recognition, Mountain Recog-
in our database. For this purpose, we have to automatically
nition, Time Series
extract the exact skyline from the image, which is a diffi-
cult task, because the segmentation of the image can lead
to artefacts, for instance due to weather conditions, noise,
obstacles or overexposure.
In this paper we introduce a baseline segmentation algo-
rithm, which uses an outlier detection algorithm to identify
and eliminate these artefacts. The article is structured as
follows: In the next section we discuss other papers related
to our algorithm. We then introduce our algorithm, which
27th GI-Workshop on Foundations of Databases (Grundlagen von Daten- consists of the three steps silhouette extraction, silhouette
banken), 26.05.2015 - 29.05.2015, Magdeburg, Germany. cleaning and silhouette matching. The section of the latter
Copyright is held by the author/owner(s).
18
one will be a perspective of how to use the cleaned silhouette 3. SILHOUETTE RECOGNITION
in further steps. The last chapter summarises this paper and The silhouette recognition process is basically a process in
outlines future work. three steps. The first step is the extraction of the silhouette
from a given picture. This silhouette is stored as a polygonal
2. RELATED WORK chain. During the second step, this silhouette is cleaned by
The amount of publications for automated mountain re- identifying and removing outliers. The cleaned silhouette
cognition increased significantly in the last two decades. is then used as the input to the third and final step, which
Given a high number of publicly available digital elevation consists of matching the silhouette against the reference data
maps (DEM), the consensus in many publications [2, 3, 4, 5, in order to be able to identify the structure in the picture.
10] is to use image-to-model matching for mountain recog-
nition and orientation identification. 3.1 Silhouette Extraction
Many approaches, like [3, 4, 10], make use of known GPS Extracting the silhouette is the first step in the mountain
data for the image to align in the terrain. This reduces the identification process described in this paper. The task of
search space for possible mountain silhouettes significantly, this step is the separation of the sky from the rest of the
because the search of the correct mountains is limited in processed image. We therefore have a binary segmentation
checking the surrounding of the camera’s position. task to solve, in which we check every pixel p of an image and
Baboud et al. [3] need an estimated field-of-view to cal- decide if p shows a part of the sky or not. For a human this
culate the rotation which maps the image to the terrain. would be easy to do in most cases, even though it would
Therefore, the authors introduce a robust matching metric, be too much work and time to segment great numbers of
using extracted edges in combination with a search space pictures manually. Because of that, we use a growing seed
reduction to further reduce computation time. [10] uses a algorithm, like the algorithm described in [11], for which we
standard Sobel-filter for the edge extraction. To identify the use the fact, that in most cases the pixels at the upper bound
edges which are part of the mountain’s contours, the authors of the image are part of the sky. In the following section,
propose the use of the Random Ferns classifier. Afterwards we will first describe some difficulties, which can make the
they match the emerged contour map with the contour map segmentation task more complex. After that, our baseline
extracted from the DEM. In [4] a 360-degree panorama of segmentation algorithm will be further explained.
the surroundings of the image location is synthesized out of The segmentation of an image generally suffers from dif-
a given DEM and used for matching the skyline extracted ferent problems, like under-/overexposure, which results in
from the image. For that, they use a vector cross correlation a smaller difference between the pixel colours, or blur, which
(VCC) technique to find the candidate matches. After fur- can be, for example, a result of a lossy compression of the
ther refinement and recalculation of the VCC for each peak image. In addition, our binary segmentation task has even
they can label the peaks with a high precision. some own problems to deal with. The first difficulty is a con-
All three approaches show good results for their issue, sequence of the motif itself, because the weather in moun-
but the need for GPS data for the processed image does tainous terrain is very volatile. This and the fact that, for
not match our problem specifications, different from Baatz example in the alps, many mountain peaks are covered with
et al. [2], in which the focus lies on images without GPS snow, can make the extraction of the mountain silhouette
tag. They use an approach based on a cost function for out of an image imprecise. Furthermore differently coloured
the belonging of a pixel to the sky respectively foreground. bands of cloud can lead to an extracted silhouette which lies
They combine this approach with a relevance feedback-like in the sky and is therefore not part of the real skyline. The
user interference, where the user can mark parts of the sky or second one is a result of possible obstacles, which hide the
the foreground. This user intervention was needed for 49% of real silhouette of the mountain or lead to smaller segments
the images in their dataset, which was collected during there within the sky segment.
research. Thankfully, they published this dataset, so that Even though we are aware of these problems, our naive
it will be used in this paper. After the contour extraction, segmentation algorithm cannot handle all of them. For the
they match the coded contourlet with the contours extracted identification of artefacts as result of segmentation errors,
from a DEM at several points on a predefined grid. At we use the second step of our chain, which is described in
last, when they find a suitable match, they recalculate the section 3.2. Our segmentation algorithm uses the upper row
geo-position of the camera. Naval et al. [5] also try to of pixels as seed for the sky segment. This means that we
find the camera position and orientation, using a DEM to mark the upper pixels as sky and process every neighbouring
position the camera on the world. For this purpose they pixel to let this segment grow. For this purpose we first
match the extracted skyline from an image with a synthetic convert the processed photo to a gray-scale image G. Now
skyline from a DEM. Different to both works we try to get we can define VG as overall variance of the brightness values.
an automatically cleaned silhouette, thus removing obstacles Having VG , we now process every pixel p(x,y) which is not a
or other artefacts, out of the processed image. part of the sky segment and mark it as sky candidate, if for
Tao et al. [12] focus on the identification of the sky seen p(x,y) it holds that
in an image and the search for images with a specific sky √
appearance. Therefore they define different sky attributes, Bp(x,y) − meanrp(x,y) < γ · VG
like for example the sun position, which they extract in-
dividually afterwards. At last they present their complete with Bp(x,y) the brightness of the pixel p(x,y) , meanrp(x,y) as
system SkyFinder, in which an attribute based search is im- the mean of the brightness in an neighbourhood of the pixel
plemented. On top of that, they provide a sky replacement with the radius of r and γ as a factor to scale the impact
algorithm for changing the sky in an image. However, the of the standard derivation. This means that we mark a
recognition of the skyline is not part of this system. pixel, if its distance to a local mean of brightness values
19
Figure 2: The result of the segmentation algorithm. (Left) The original image out of the dataset from [2].
(Right) The binary image after the segmentation. White pixels mark the ground segment.
is smaller than a fixed percentage of the global standard describes the y-coordinate of that point in the picture. We
derivation of all brightness values. The idea behind this start at the upper left pixel p(0,0) and search for the first non-
term is, that the border between sky and ground has in sky pixel as start vertex v1 for our polygonal chain, which
most cases a stronger contrast than the rest of the image, lies on the left pixel column with x1 = 0. Now, we have
especially the sky. Therefore, the distance to the mean will two possibilities: First, we can find no pixel, which results
be higher at the skyline as it will be at the border of possible in checking the next column until we find a pixel or we have
clouds. With the connection of the upper bound to the reached the lower right pixel of the image. Otherwise, if a
standard derivation, we want to take into account the images possible skyline pixel was found, the algorithm tracks the
where the brightness is very homogenous, for example due transition between the sky and the ground in the following
to overexposure, and therefore the contrast of the skyline manner.
decreases. Having vi as the last extracted vertex of the silhouette, the
In our experience this naive assumption shows good re- algorithm checks the 8-connected neighbours of this point
sults for r = 5 and γ = 0.1 on most images out of the swiss (see the right side of figure 1) and chooses the non-sky point
dataset published in [2]. In the future we will test more as next vertex which has the lowest angle between the in-
complex algorithms, like for instance the skyline extraction coming line, defined by the two last vertices vi−1 and vi ,
algorithm proposed in [8], with the expectation that they and the outgoing line, defined by vi and the neighbouring
will yield even better results. After we have marked all pos- point. This can easily be done, as shown in figure 3, by
checking the 8-connected neighbours from vi in clockwise
y
direction, starting at the predecessor. The only exception is
−1 the start point v1 , where we set v0 = (−1, y1 ). This means
that we choose in this case the left direct neighbour, which
p 0 p lies outside of the image, as predecessor.
1
x 2 3 4
−1 0 1 −1 0 1
1 vi 5
Figure 1: Two possible neighbourhoods of a pixel.
(Left) 4-connected neighbourhood of the pixel p.
vi−1 0 7 6
(Right) 8-connected neighbourhood of the pixel p.
Figure 3: An example how to choose the next silhou-
sible candidates, we can finally let the sky segment grow. For ette point. Starting at vi−1 as predecessor of the cur-
that, we check for every pixel p(x,y) , if it is a 4-connected rent vertex vi , the algorithm searches in clockwise
neighbour, for explanation see the left side of figure 1, to a direction for the next ground pixel (blue). Therefore
pixel of the sky segment and marked as sky candidate (see the third pixel will be chosen.
the previous step). If so, the pixel will get marked as sky.
This step is repeated until no more pixels can be added to
the sky segment. At this point, we have a binary image,
which represents the skyline as transition between the sky Now, we have the border of the mountain as chain of
and the ground, like the one shown in figure 2. neighbouring pixels. To reduce the amount of vertices with-
For the silhouette extraction, we search a silhouette S = out loosing any information, we eliminate all vertices which
(v1 , . . . , vn ) where every vertex vi = (xi , yi ) is a two di- bring no further information gain to the final silhouette in
mensional point where xi describes the x-coordinate and yi the last step of the extraction phase. For this, we define the
20
Figure 4: Silhouette as black line with outliers in red on original image.
information gain I of a vertex vj , with 1 < j < n, as get recognised as mountain. Since the outline of this outlier
( is not atypical for a mountain silhouette, this outlier is not
1, if ∠vj−1 vj vj+1 6= 180◦ recognised. Farther to the right, there are two other outliers
Ivj =
0 otherwise. where clouds are recognised as mountain by the extraction
step. These are, in this case, marked red and such detected
This means that every vertex which lies on one line with his by the silhouette cleaning step.
predecessor and successor carries no further information for Note, that there are a few parts of the silhouette that
the extracted silhouette. After deleting every vertex with get marked as outliers but are not described above. These
Ivj = 0, we obtain our final polygonal chain, which can are false positives. However, since the image here is used
now be analyzed in the the cleaning phase described in the to showcase the different kind of outliers, these are not dis-
following section. cussed here.
The silhouette cleaning part of our algorithm is, again,
3.2 Silhouette Cleaning divided into three parts. As an input, it gets the silhouette
The extracted silhouette from the previous step may con- S = (v1 , . . . , vn ) extracted from a picture.
tain different disturbances. In this step, we try to get rid For the recognition of patterns inside the polygonal chain,
of those, so that they cannot affect the step of silhouette this representation has disadvantages, because all vertices
matching that gets described in section 3.3. Examples for are given in absolute positions. For outlier detection, a
such disturbances are manifold, ranging from objects in front model where similar structures do look similar in their rep-
of the mountain, such as trees, to errors produced during resentation is beneficial. Therefore, we convert S to a rep-
the extraction process that can be caused by a low contrast resentation AP = (ap1 , . . . , apn ), with api = (li , ai ). Here,
between the mountain line and the sky. Essentially, after we set
finding an outlier, we currently cut elevations within this (
part of the silhouette away. |vi − vi−1 |, if i > 1
li =
Some of such outliers are showcased in figure 4. Here, the 0 else
black line is the silhouette that has been extracted by the
extraction step as described in section 3.1. The red parts are and ai as the angle between the vector vi+1 − vi , and the
those parts of the silhouette, that are automatically detected x-axis for i < n and an = 0◦ . Figure 5 illustrates this.
as outliers. Examples for outliers are the tree tops to the During this step, points where the angle does not change
left, that have correctly been marked as outliers. In the are removed. Also, artefacts consisting of angles of 180◦
center of the picture, there is a part of the mountain, that between two following segments get removed.
has been cut out. Here the contrast between rock and blue The basic idea of the outlier detection method itself is
sky gets to low for the extraction step to distinguish them. to compare histograms created from parts of the polygonal
Right next to this, clouds around the lower peak in the back chain AP to a reference histogram. The latter hereby is
created from the silhouettes of the ground truth data as
given in [2].
v2 The mentioned histograms consist of the two dimensions
a2 = −45◦ segment length and angle size, just as the points in the
|
transformed polygonal chain AP , and are normalised. This
v1
−
l3
means, that the sum of the frequencies of each bucket is 1.
2
=
|v
|v 3
=
For the reference data, we compute one histogram from each
l2
−
image’s silhouette. Finally, the mean of all these histograms
v2
|
a1 = 45◦ is computed and becomes the reference histogram Hr .
l1 = 0 v1 v3 a3 = 0◦
In order to find outliers, we use a sliding window ap-
proach over the polygonal chain APi of a given input im-
Figure 5: Conversion of polygonal chain for better age. We therefore use a section of m successive segments
pattern recognition. api , . . . , api+m−1 in order to compute a histogram Hi with
the same bucket distribution as in Hr . Then, for every point
21
used to compute Hi , we store the distance between Hi and so that we find a vertex ve . This results in four vertices’
Hr . By this approach, for most points, multiple distance indices j ≤ s < e ≤ j 1 . From this, the part between vs and
values get stored when the sliding window is moved on. The ve is now finally replaced by a straight line between these
final distance di for a point api is now computed as the two vertices.
average distance of all distances stored for this point. By this, we obtain a cleaned silhouette that can be used
As distance function in this case we use the one given in in further steps for the silhouette matching.
the following
3.3 Silhouette Matching
Definition 1. Let G = (g1 , . . . , gk ), H = (h1 , . . . , hk ) be Once the silhouette is extracted and cleared of outliers,
normalised histograms with the same bucket distribution. it is now time to match it to the reference data in order to
The above average distance of G to H is defined by determine the mountain that is shown by the picture. Cur-
D(G, H) := max(|aab(G)|, |aab(H)|) − |aab(G) ∩ aab(H)|, rently, we have not implemented any method, yet. However,
we will introduce some methods that we are going to test in
where the future.
1 The converted silhouettes AP = (ap1 , . . . , apn ) from the
aab(F ) := i ∈ {1, . . . , k} fi ≥
k previous section can be easily changed to be interpreted as
time series by setting AP 0 = (ap01 , . . . , ap0n ) where
with F being a normalised histogram with k buckets.
Xi
This can be implemented to compute in linear time to the ap0i = (li0 , vi ) = ( li , v i )
number of buckets, or, for histograms of a fixed length, in j=1
constant time.
Based on a number of images used as training data, in for api = (li , vi ). By this conversion, every point ap0i has
the same way as described above, the medium distance µ the length of the polygonal chain until that point as its first
and the standard deviation σ can be determined. We use component. Since li > 0 for all i > 1, the length component
these precomputed values in order to find two thresholds of AP 0 is strictly monotonic increasing, just as the time
τin > τout , such that we can characterise a point api as a dimension in a time series. This is because we do not allow
strong anomaly if it holds that its distance identical points in our silhouette. With this, it is possible
to compare different silhouettes for similarity by using time
di ≥ µ + τin · σ series comparison techniques such as [6, 9]. These methods
and a weak anomaly if have the advantage of being rotation invariant which, in our
case, means that the image does not have to have the same
di ≥ µ + τout · σ. image section as our reference data.
We also determine a length threshold l. For a part of the Due to the great number of mountains and peaks that ex-
polygonal chain, to be recognised as an outlier, it must hold ist and on the notion, that every peak can be photographed
that at least l successive points api , . . . , api+l−1 must be from different angles, there are hundreds of thousands of sil-
strong anomalies. An outlier can have any length that is houettes to match for each query image. Due to this it is
equal to or bigger than l. Finally, if we find such an outlier, clear, that even with a high-performance matching method
we expand it by adding points to it that are adjacent to a preselection is necessary.
the outlier and weak anomalies. By this, it is possible for There exist many methods suitable for this, such as the
outliers to merge. technique presented in [7] that uses the position of the sun
As an example, say, ap7 , . . . , ap20 are strong anomalies and the time of day, at which the photo has been taken, in
because their distances d7 , . . . , d20 are bigger than µ+τin ·σ. order to compute the approximate location on earth. How-
If we set l = 4 for this example, these thirteen points are ever, this method has an average localisation error of about
recognised as an outlier o = {7, . . . , 20}. Now, let us assume 100 km. Because of this, it is useful for a rough preselection,
that ap5 and ap6 are weak anomalies as well as ap22 . Then however not sufficient in our case.
ap6 and ap5 belong to the extended outlier, because they Therefore, we aim to reuse the idea of our outlier de-
are both adjacent to the outlier. On the other hand, ap22 tection method for matching. Instead of computing his-
does not belong to the extended outlier, because ap21 is not tograms of short sequences of the silhouette, in this case the
part of the outlier since it is not a weak anomaly. histogram HS over the whole silhouette AP is computed.
Once all outliers have been detected, the next step is to This is then compared to the reference images’ histograms
remove them. Currently, we use a very simple approach for HRi , i ∈ {1, . . . , nref } with nref the number of the reference
this where, in the original silhouette S, the overhanging part image, which we, as discussed in section 3.2, have to compute
of the outlier is replaced by a straight line. This is based on anyway. The comparison between two histograms, with our
the notion, that the reasons for most outliers in the silhou- distance function, is linear to the number of buckets in the
ette are either trees or clouds. By just removing them, in histograms, or, since this number is fixed, constant. Now,
many cases, we get results that resemble the original shape let d(HS , HRi ) denote the distance between the histogram
of the mountain in an easy way. of the new silhouette S and the ith reference histogram. If
Let o = {i, . . . , j} with i + l ≤ j be an outlier. Then we d(HS , HRi ) is small, this does not necessarily mean, that
draw a straight line from vi to vj . Now, while the distance the silhouettes are identical or even really similar, because
between vs , starting with s = i + 1, and that straight is the histogram representation does not preserve the order of
smaller than a preset value d, we find the vertex vs with the the contained elements. On the other hand, if the distance
largest index, that is close enough to the original straight 1
In general, it is possible, that s ≥ e. In this case, no
line. The same is done from the other end of the outlier substitution is performed.
22
between two silhouettes is large, we can say that those sil- buildings are mostly photographed with the sky as back-
houettes are not similar. ground, too. Furthermore, we aim to test our method on
Due to this, we plan to only use the time series compar- more diverse areas, such as the recognition of certain ob-
ison methods from the beginning of this section on those jects in MRT screenings or X-rays. These tasks will make
reference silhouettes, that yield small histogram distances. it necessary to change some parts of our approach, natu-
Further on, we will evaluate if the position determination rally, because there, it will be interesting to be able to tell,
method presented in [7] is able to boost the performance of for example, which organs are shown in a picture, or, as a
our solution. next step, to be able to identify different kinds of tumours
automatically.
4. CONCLUSION AND FUTURE WORK
In this work we presented a new approach to motif recog- 5. REFERENCES
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