=Paper=
{{Paper
|id=Vol-1178/CLEF2012wn-ImageCLEF-BorosEt2012
|storemode=property
|title=UAIC Participation at Robot Vision @ 2012 - An Updated Vision
|pdfUrl=https://ceur-ws.org/Vol-1178/CLEF2012wn-ImageCLEF-BorosEt2012.pdf
|volume=Vol-1178
|dblpUrl=https://dblp.org/rec/conf/clef/BorosGI12
}}
==UAIC Participation at Robot Vision @ 2012 - An Updated Vision ==
https://ceur-ws.org/Vol-1178/CLEF2012wn-ImageCLEF-BorosEt2012.pdf
UAIC participation at Robot Vision @ 2012
An updated vision
Emanuela Boroş, Alexandru Lucian Gı̂nscă, and Adrian Iftene
Alexandru Ioan Cuza University, Faculty of Computer Science
General Berthelot, 16, 700483, Iaşi, Romania
{emanuela.boros,lucian.ginsca,adiftene}@info.uaic.ro
Abstract. In this paper we describe a system that participated in the
fourth benchmarking activity ImageCLEF, in the Robot Vision task, for
which we approach the task of topological localization without using a
temporal continuity of the sequences of images. We provide details for
the state-of-the-art methods that were selected: Color Histograms, SIFT
(Scale Invariant Feature Transform), ASIFT (Affine SIFT) and RGB-
SIFT, Bag-of-Visual-Words strategy inspired from the text retrieval com-
munity. We focused on finding the optimal set of features and a deepened
analysis was carried out. We offer an analysis of the different features,
similarity measures and a performance evaluation of combinations of the
proposed methods for topological localization. Also, we detail a genetic
algorithm that was used for eliminating the false positives results. In
the end, we draw several conclusions targeting the advantages of using
proper configurations of visual-based appearance descriptors, similarity
measures and classifiers.
Keywords: Robot Topological Localization, Global Features, Invariant
Local Features, Visual Words, SVMs, Genetic Algorithm
1 Introduction and Related Work
In this paper, we present an approach to vision-based mobile robot localization
that uses a single perspective camera taken within an office environment. The
robot should be able to answer the question where are you? when presented with
a test sequence representing a room category seen during training [30, 33, 25].
We analyze the problem without taking in consideration the use of the temporal
continuity of the sequences of images. We perform an exhaustive evaluation and
introduce a new analysis statistic between quantization techniques of a large set
of features, from which different system configurations are picked and tested.
Traditionally, robot vision systems heavily relied on different methods for
robotic topological localization such as topological map building which makes
good use of temporal continuity [37], panoramic vision creation [38], simultane-
ous localization and mapping [7], appearance-based place recognition for topo-
logical localization [38], Monte-Carlo localization [41].
The problem of topological mobile localization has mainly three dimensions:
a type of environment (indoor, outdoor, outdoor natural), a perception (sensing
2 UAIC participation at Robot Vision @ 2012
modality) and a localization model (probabilistic, basic). Numerous papers deal
with indoor environments [37, 38, 10, 21] and a few deal with outdoor environ-
ments, natural or urban [36, 13].
Current work on robot localization in indoor environments has focused on
introducing probabilistic models to improve local feature matching and the inte-
gration of specific kernels. Experimental results for wide baseline image match-
ing suggest the need for local invariant descriptors of images. Invariant features
have achieved relative success with object detection and image matching. There
has also been research into the development of fully invariant features [4, 26,
27]. In his milestone paper [23], D. Lowe has proposed a scale invariant feature
transform (SIFT) that is invariant to image scaling and rotation, illumination
and viewpoint changes. Lately, a new method has been proposed, Affine-SIFT
(ASIFT) that simulates all the views obtainable by varying the two camera axis
orientation parameters, namely the latitude and the longitude angles [29].
The Bag-of-Visual-Words [8, 12] model is a great addition to place recogni-
tion and was initially inspired by the bag-of-words models in text classification
where a document is represented by an unsorted set of the contained words. This
data modeling technique was first been introduced in the case of video retrieval
[35]. Due to its efficiency and effectiveness, it became very popular in the fields
of image retrieval and classification [20, 43].
The classification level of images relies more on unsupervised then supervised
learning techniques. Categorizing in unsupervised learning scenarios is a much
harder problem, due to the absence of class labels that would guide the search
for relevant information. In supervised learning scenarios, image categorizing has
been studied widely in the literature. Among supervised learning techniques, the
most popular in this context are Bayesian classifiers [8, 18, 12, 19] and Support
Vector Machines (SVM) [39, 8, 18, 44]. [3] also uses random forests. Actually,
state-of-the-art results are due to SVM classifiers: the method described in [44]
combines a local matching of the features and specific kernels based on the Earth
Movers Distance [32] or χ2 [28] yielded the best results.
Our approach represents an extension of our previous work [1, 2] where each
RGB image is processed to extract sets of SIFT keypoints from where the de-
scriptors are defined. Making use of global and local features, a quantization
technique, SVMs and a genetic algorithm that aims at eliminating the false pos-
itives, we approached the task of recognition with different configurations and
the one that got the best results has been reviewed in the 2012 Robot Vision
task in ImageCLEF international campaign.
2 Image Analysis
In this section, we describe the image features that have been used in this work
in order to obtain a precise and effective model for the topological localization
task. In order to obtain an image representation which captures the essential
appearance of the location and is robust to occlusions and changes in image
brightness, we compare two different image descriptors and their associated dis-
UAIC participation at Robot Vision @ 2012 3
tance measure. In the first case, we use color histograms integrated and in the
second case each image is represented by a set of local scale-invariant features,
quantized in bags of visual words.
2.1 Global Features
Many recognition systems based on images use global features that describe the
entire image, an overall view of the image that is transformed in histograms of
frequencies. Adopting the analysis of global features has brought great improve-
ment in robot localization systems as in [33] or in content based image retrieval
systems as in medical related images analysis in [34]. Such features are impor-
tant because they produce very compact representations of images, where each
image corresponds to a point in a high dimensional feature space.
In the following, we attempt to model image densities using two different
color spaces, RGB and HSV.
RGB (Red, Green, and Blue) Color Model is composed of the primary
colors Red, Green, and Blue. They are considered the additive primaries since
the colors are added together to produce the desired color. White is produced
when all three primary colors at the maximum light intensity (255). The RGB
space has the major deficiency of not being perceptually uniform, this being the
motivation of adding HSV color histograms.
HSV (Hue, Saturation, and Value) Color Model defines colors in terms
of three constituent components: hue, saturation and value or brightness. The
hue and saturation components are intimately related to the way human eye
perceives color because they capture the whole spectrum of colors. The value
represents intensity of a color, which is decoupled from the color information
in the represented image. This color model is attractive because color image
processing performed independently on the color channels does not introduce
false colors (hues). However, it has also inconvenient due to the necessary non-
linearity in forward and reverse transformations with RGB space.
A color histogram denotes the joint probabilities of the intensities of the three
color channels and is computed by discretizing the colors within the image and
counting the number of pixels of each color. Since the number of colors is finite,
it is usually more convenient to transform the three channel histogram into a
single variable histogram, therefore a quantization of the histograms is needed.
The histogram dimension (the number of histogram bins) n is determined by the
color representation scheme and quantization level. Most color spaces represent
a color as a three-dimensional vector with real values (e.g. RGB, HSV). We
quantize the color space of three axes into k bins for the first axis, l bins for the
second axis and m bins for the third axis. The histogram can be represented as
an n-dimensional vector where n = k · l · m. Because the retrieval performance is
saturated when the number of bins is increased beyond some value, normalized
color histogram difference can be a satisfactory measure of frame dissimilarity,
even when colors are quantized into only 64 bins (4 Green × 4 Red × 4 Blue).
As a conclusion, we chose a 18 · 10 · 10 multidimensional HSV histogram, and
a 10 · 10 · 10 multidimensional RGB histogram, as differences between colors of
4 UAIC participation at Robot Vision @ 2012
the office environment have a high level of similarity and have slight changes in
hues.
2.2 Local Features
A different paradigm is to use local features, which are descriptors of local image
neighborhoods computed at multiple interest points. There are many local fea-
tures developed in the last years for image analysis, with the outstanding SIFT
as the most popular. In the literature, there are several works studying the differ-
ent features and their descriptors, for instance [22] evaluates the performance of
local descriptors, and [44] shows a study on the performance of different feature
for object recognition.
The three types of features used in our experiments are SIFT (Scale Invari-
ant Feature Transform), ASIFT (Affine Scale Invariant Feature Transform) and
RGB-SIFT (RGB Scale Invariant Feature Transform).These features were ex-
tracted using [14]. Also, the localization experiments using these features show
advantages and disadvantages of using one or another.
SIFT (Scale Invariant Feature Transforms)[23, 4, 24] features corre-
spond to highly distinguishable image locations which can be detected efficiently
and have been shown to be stable across wide variations of viewpoint and scale.
The algorithm basically extracts features that are invariant to rotation, scaling
an partially invariant to changes in illumination an affine transformations. This
feature has been explained in our previous work being one of our key level of
our systems [1, 2].
ASIFT (Affine Scale Invariant Feature Transforms), as described in
[29], simulates with enough accuracy all distortions caused by a variation of the
camera optical axis direction. Then it applies the SIFT method. In other words,
ASIFT simulates three parameters: the scale, the camera longitude angle and the
latitude angle and normalizes the other three (translation and rotation), what
SIFT lacked.
RGB-SIFT (RGB Scale Invariant Feature Transforms) descriptors
are computed for every RGB channel independently. Therefore, each channel
is normalized separately which brings another important aspect for SIFT, the
invariance to light color change. For a color image, the SIFT descriptions inde-
pendently from each RGB component and concatenated into a 384-dimensional
local feature (RGB-SIFT) [5].
2.3 Feature Matching
In this subsection we introduce different dissimilarity measures to compare fea-
tures. That is, a measure of dissimilarity between two features and thus between
the underlying images is calculated. Many of the features presented are in fact
histograms (color histograms, invariant feature histograms). As comparison of
distributions is a well known problem, a lot of comparison measures have been
proposed and compared before [31].
UAIC participation at Robot Vision @ 2012 5
In the following, dissimilarity measures to compare two histograms H and
K are proposed. Each of these histograms has n bins and Hi is the value of the
i-th bin of histogram H.
– Minkowski-form Distance (L1 distance is often used for computing dis-
similarity between color images, also experimented in color histograms com-
parison [17]):
1
X
DLr (H, K) = ( |Hi − Ki |) r (1)
i=1
– Jensen Shannon Divergence (also referred to as Jeffrey Divergence [9],
is an empirical extension of the Kullback-Leibler Divergence. It is symmetric
and numerically more stable):
X 2Hi 2Ki
DJSD (H, K) = Hi log + Ki log (2)
i=1
Hi + Ki Ki + Hi
– χ2 Distance (measures how unlikely it is that one distribution was drawn
from the population represented by the other, [28]):
X (Hi − Ki )2
Dχ2 (H, K) = (3)
i=1
Hi
– Bhattacharyya Distance [6] (measures the similarity of two discrete or
continuous probability distributions). For discrete probability distributions
H and K over the same domain, it is defined as:
Xp
DB (H, K) = − ln (Hi Ki ) (4)
i=1
3 Classification
Many of the features presented in Section 2 are in fact histograms (color his-
tograms, invariant feature histograms, texture histograms, local feature his-
tograms). As comparison of distributions is a well known problem, a lot of com-
parison measures have been proposed in Section 2.3. To analyze the different
measure distances we summarize a well known choice for supervised classifica-
tion.
Support Vector Machines are the state-of-the-art large margin classifiers
which recently gained popularity within visual pattern and object recognition
[15, 8, 18, 44, 40, 42]. Choosing the most appropriate kernel highly depends on the
problem at hand - and fine tuning its parameters can easily become a tedious
task. For our experimental setup, we chose the linear kernel (which is trivial
and won’t be presented), the radial basis function and the χ2 kernel, presented
below.
6 UAIC participation at Robot Vision @ 2012
The Gaussian Kernel is an example of radial basis function kernel.
kx − yk2
Kg (x, y) = exp − (5)
2σ 2
The χ2 Kernel comes from the χ2 distribution.
n
X (xi − yi )2
Kχ2 (x, y) = 1 − 1 (6)
i=1 2
(xi + yi )
3.1 Bag-of-Visual-Words (BoVW)
Recent advances in the image recognition field have shown that bag-of-visual-
words [8, 12] - a strategy that draws inspiration from the text retrieval com-
munity - approaches are a good method for many image classification problems.
BoVWs representations have recently become popular for content based image
classification because of their simplicity and extremely good performance.
Basically, to give an estimation of the distribution we create histograms of
the local features. The key idea of the bag-of-visual-words representation is to
quantize each keypoint into one of the visual words that are often derived by
clustering. Typically k-means clustering is used. The size of the vocabulary k
is a user-supplied parameter. The visual words are the k cluster centers. The
baseline of our tests are based on a bag-of-visual-words with a 100 visual words,
meaning a 100-means clustering. The resulting k n-dimensional cluster centers
cj represent the visual words.
4 Experimental Setup
In this section, we explain the experimental setup, then we present and dis-
cuss the results. The different choices of distance measures and classification
parameters are analyzed performing also a comparison with previous work re-
sults. Conclusions are drawn in benefit of an accurate solution for topological
localization, data modeling and classification.
4.1 Datasets (Benchmark)
The chosen dataset contains images from nine sections of an office obtained from
CLEF (Conference on Multilingual and Multimodal Information Ac-
cess Evaluation). Detailed information about the dataset are in the overviews
and ImageCLEF publications [30, 33, 25]. The dataset has already been split into
three training sets of images, as shown in Table 1 one different from another.
The provided images are in the RGB color space. The sequences are acquired
within the same building and floor but there can be variations in the lighting
conditions (sunny, cloudy, night) or the acquisition procedure (clockwise and
counter clockwise).
UAIC participation at Robot Vision @ 2012 7
Areas # Images
training1 training2 training3
Corridor 438 498 444
ElevatorArea 140 152 84
LoungeArea 421 452 376
PrinterRoom 119 80 65
ProfessorOffice 408 336 247
StudentOffice 664 599 388
TechnicalRoom 153 96 118
Toilet 198 240 131
VisioConference 126 79 60
Table 1. Training Sequences of An Office Environment
4.2 Eliminating False Positives
Finally, a method for the elimination of the unwanted results is performed, there-
fore the retrieved classes for images (Corridor, LoungeArea etc.) depend on a
threshold, those below this value being rejected, with the meaning that the
system doesn’t recognize the image. This becomes an optimization problem of
finding the best value that will cut the unwanted results, considering that it is
better to have no results than inconsistent results.
We adapted the implementation of the genetic algorithm described in [11]. In
order to capture the particularities of the distance measures that are correlated
with the rooms on which they are used, we considered a different threshold for
each room. As a justification for choosing multiple thresholds rather than a single
one, let us consider the case in which we are trying to classify images taken from
a room that is more distinguishable from the others. The values returned by
the similarity measures when comparing these images to others taken from the
same room are further apart from the values returned in the case of comparing
these images with others taken from different rooms. In contrast, if we consider
a room that is visually similar to others, these values will be closer on the real
axis. This is why it is harder to correctly separate erroneous classifications for
the good ones with a single threshold.
For the genetic algorithm, the chromosomes are vectors of length 9, represent-
ing the thresholds for the 9 rooms. For the genetic operators we used the binary
representation of these vectors. The fitness function evaluates the quality of the
thresholds and it is the measure used to score runs in the Robot Vision task. As
a selection strategy, we used the rank selection, which sorts the chromosomes
accordingly to their value given by the fitness function. In the crossover process,
we don’t allow the parent chromosomes which are the input for the crossover
to be the same individual as it could lead to early convergence. To prevent this
from happening, we first select one chromosome from the population and then
run the selection process in a loop until a different chromosome is returned. We
also used elitism in order to assure the survival of the best chromosomes of each
8 UAIC participation at Robot Vision @ 2012
generation. In order to balance the diversity of the population, this method is
accompanied by a slightly increased mutation probability.
For these experiments, we used a population of 200 individuals, the mutation
probability of 0 : 15, and the crossover, of 0 : 7. The optimization process is
stopped after 1000 generations.
4.3 Results Interpretation
We are interested in observing the performances of the final configurations to
see which features/dissimilarity measures lead to good results and which do
not. As it is well known that combinations of different methods lead to good
results [16], an objective is to combine the briefly presented features. However,
it is not obvious how to combine the features. To analyze the characteristics of
Method R[%] P[%] F
RGB-Only 73.73 82.02 0.77
HSV-Only 76.46 82.34 0.79
RGB-HSV 76.42 79.66 0.780
Basic-BoVW-SIFT 45.10 46.51 0.45
Basic-BoVW-SIFT+HSV+RGB 76.85 79.26 0.780
Basic-BoVW-ASIFT+HSV+RGB 77.60 79.97 0.787
SVM-RBF-BoVW-SIFT+HSV+RGB 78.87 78.87 0.788
SVM-LINEAR-BoVW-SIFT+HSV+RGB 78.63 78.94 0.787
SVM-χ2 -BoVW-SIFT+HSV+RGB 78.43 78.52 0.784
Table 2. Performance Comparison for Topological Localization
features and which features have similar properties, we perform an evaluation
on selected configurations as shown in Table 2. The evaluation was performed
choosing Training 1 and 3 (Table 1) for training and Training 2 for testing.
The first column gives a description of the used training method. The de-
scriptions of the configurations are straight forward, for example, Basic-BoVW-
SIFT+HSV+RGB means a configuration of a combination of RGB and HSV
color histograms and Basic-BoVW-SIFT a bag of visual words formed with
SIFT feature vectors. The chosen measure distances were decided like this: Jef-
frey Divergence for RGB histograms, Bhattacharyya for HSV histograms and
Minkowski for SIFT feature vectors. The second column gives the recall values
for the training data, the third - the precisions. The F-measure is computed and
represented in the fourth column of the table. The table also shows that feature
selection only is not sufficient to increase the recognition rate but more flexibility
is needed here and this fact led to different combinations.
The results are improved by the addition of the SVM classification step. We
also add the observation that a SVM classification of SIFT mapped on visual
words can get to a maximum of 52% accuracy, but these results are very assuring
UAIC participation at Robot Vision @ 2012 9
in the context of a configuration in which are implied the usage of other feature
descriptors. Thereby, the configuration that combines SIFT words, HSV and
RGB histograms and a classification with a SVM with a RBF kernel yielded the
most satisfying result.
4.4 ImageCLEF 2012 Robot Vision Task
The fourth edition of the Robot Vision challenge focused on the problem of
multi-modal place classification. We had to classify functional areas on the basis
of image sequences, captured by a perspective camera and a kinect mounted on
a mobile robot within an office environment with nine rooms. We ranked third
out of seven registered groups.
# Group Score
1 CIII UTN FRC, Universidad Tecnológica Nacional, Ciudad Universitaria, Cor- 2071.0
doba, Argentina
2 NUDT, Department of Automatic Control, College of Mechatronics and Au- 1817.0
tomation, National University of Defense Technology, China
3 Faculty of Computer Science, Alexandru Ioan Cuza University 1348.0
(UAIC), Iaşi, România
4 USUroom409, Yekaterinburg, Russian Federation 1225.0
5 SKB Kontur Labs, Yekaterinburg, Russian Federation 1028.0
6 CBIRITU, Istanbul Technical University, Turkey 551.0
7 Intelligent Systems and Data Mining Group (SIMD), University of Castilla-La 462.0
Mancha, Albacete, Spain
8 BuffaloVision, University at Buffalo, NY, United States -70.0
Table 3. ImageCLEF 2012 Robot Vision final results
5 Discussion
Our approach on topological localization is currently applied on an office envi-
ronment of nine sections: Corridor, ProfessorOffice, StudentOffice, LoungeArea,
PrinterRoom, Toilet, VisioConference, ElevatorArea and TechnicalRoom. To ad-
dress the problem of recognizing these sections separately, we approached the
classification with specific thresholds in taking the final decision over the selected
room. These thresholds create constraints that have to be loosened in order to
obtain an accurate result in treating situations of great similarity between two
different rooms. As an example, note that one of the main inconvenient that can
appear in this case is that the rooms are very connected and difficult situations
can rise as the robot moves around the office. For example, if the robot is in the
Corridor, it looks to its right and sees the LoungeArea but its position is still
in the Corridor. This type of situation creates noise that cannot be neglected,
10 UAIC participation at Robot Vision @ 2012
therefore a proper threshold needs to treat these results that correspond to a
humanized interaction with the medium. The threshold on the final decision
quality was chosen to avoid erroneous localizations, thus favoring a result that
doesn’t specify any room and giving less correct localizations but also, less false
assumptions.
6 Conclusions
In this work, we approached the task of topological localization without using a
temporal continuity of the images and involving a broad variety of features for
image recognition. The provided information about the environment is contained
in images taken with a perspective color camera mounted on a robot platform
and it represents an office environment dataset offered by ImageCLEF.
The main contribution of this work stays in quantifiable examinations of a
wide variety of different configurations for a computer vision-based system and
significant results. The experiments show that the configurations from different
feature descriptors and distance measures depend on the proper combinations.
From the fact that most of the works cited are from the last couple of years,
topological localization is a new and active area of research, which is increasingly
producing interest and enforces further development. An important contribution
to this field is given in this paper, along with notable experimental results, but
there is still room for improvement and further research.
7 Acknowledgement
The research presented in this paper was funded by the Sector Operational
Program for Human Resources Development through the project “Development
of the innovation capacity and increasing of the research impact through post-
doctoral programs”POSDRU/89/1.5/S/49944.
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