International Journal of Engineering Trends and Technology (IJETT) – Volume 34 Number 2- April 2016
Using supervised technique for label (tag)
refining of weak labels of web facial images
Ms Patel Safiya#1
#
Student, Department of Computer Science & Engg at DIEMS, Aurangabad, India
Abstract: Today’s world is the world of smart
phones, internet, multimedia which has made the art
of capturing images or image data very immense as
well as easy.
This has flooded the web with the ample of
images all over & the problem of annotating the
images and labeling them arises too. Sometimes, it
may happen that we may search the web for a
person’s image but we may get some different or
irrelevant image because the labels of these images
may be weak, noisy or with incomplete names.
Therefore, this paper aims to give the proper labels
of the facial images using supervised learning
method through SBFA methodology. We aim to use
supervised learning as it proves to be advantageous
over other learning methods & give results far better
than unsupervised ULR methodology.
Keywords — SBFA, supervised learning, SVM,
KNN
I.
INTRODUCTION
In today’s world of media and smart
phones and the era of social networking, where
photo tagging [11] and photo sharing have become
popular. Therefore, this has motivated the concept of
face annotation which helps the users to tag photos,
share and search them online and annotate them
easily. This can also be helpful in security purpose
for identifying or retrieving the images of particular
person from videos and news channels [3] or for
other purposes. Therefore annotation of facial
images and refining their weak labels serves as an
important study.
The annotation of facial images through SBFA
involves the steps as follows.
1) Collection of images from the web.
We will collect some random images of some
celebrities or some known personalities from the
WWW which consists of irrelevant images with
noisy or weak labels names.
2) Preprocessing on an image
Pre-processing technique will extract the details
from the images like face alignment, detection of
face, face region extraction, facial features etc. with
the help of GIST feature extraction techniques.
3) Identifying the label of facial image
ISSN: 2231-5381
The labels which are weakly labeled in the database
with the help of supervised techniques.
We aim to compare the two supervised
techniques such as KNN and SVM along with
unsupervised
technique [1] [6] to predict the
model of classifiers for labeling the image. The
advantage of using supervised learning method is
that
training data available for supervised
learning is in abundance, also provide reasonably
accurate classifiers and also helps in risk
minimization [8]
In the previous paper we use the SBFA
architecture for annotating the web facial images and
refining the labels through supervised learning
method. But in this paper, we mainly focus on label
refinement process through KNN and SVM
supervised learning methods and comparing the
results with unsupervised (ULR) method.
Section II reviews the literature survey carried
out for producing the proper experimentation
results.
Section III shows the experimentation carried
out for annotating the labels of facial images
from the database which are weakly labeled
through supervised methods.
Section IV shows performance evaluation based
on the result
Section V draws conclusion to the paper and
specifies limitations and future scope.
II. LITERATURE SURVEY
The work of this paper is related to many
areas of research as its aspect is broader one. The
first step is the image collection from the web which
consists of weak or incomplete labels. So acquiring
facial images from the web and processing them are
on the task. They simply can be accumulated
through various searching techniques from the web.
The next group serves the face detection and
verification techniques of the web facial images.
Recent studies in this domain have attracted the
scholars and researchers, they have come up with
some benchmarks such as LWF in recent times
[10].More research shows advancement in face
recognition techniques for annotation of tasks and its
verification. There are various image annotation
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International Journal of Engineering Trends and Technology (IJETT) – Volume 34 Number 2- April 2016
techniques existing for classification of training
models and refinement of their labels.
The next group is about the studies of image
annotation. There are various classical approaches
for generic image annotation. Various scholars have
proposed various works and research in this domain
[13][1].Some proposed to annotate images using
model based annotation results [5], others proposed
to annotate unlabelled images through iterative label
propagation scheme also[12]. Recent studies has led
the scholars to come up with the SBFA paradigm
which has attracted many more attention
[9][11].Also Dayong Wang et al. has proposed a
novel approach for refinement of labels
automatically called as unsupervised label
refinement (ULR) scheme[13].
In this paper we tend to use supervised technique for
refining the (weak) labels of images through various
supervised methods and compare the result with
ULR method proposed by [15].
III. EXPERIMENTATION
We are using Matlab 2015 for our
experimentations. Firstly we collected random
images of various celebrities into a database from
the web; we refer this image database as “retrieval
image database” Therefore retrieval image database
now consists of multiple random images with their
weak labels. In order to track the different
processing time used by different learning methods,
we have created separate database varying in
number of images and actors. So we created 3 image
databases with number of images as 200, 285 and
335 consisting of 4, 4, 5 actors respectively. For the
test dataset we divide are same retrieval image
database in ratio of 80:20 where 80% consists of
retrieval dataset and 20% as test dataset. The next
task is applying pre-processing technique on images
such as face detection and recognition. As we are
working only on facial images, we need to detect the
faces from the random image which is done using
Viola Jones algorithm. We extract the GIST features
of images using HOG (Histogram Oriented Gradient)
features to detect object in the image. With the help
of the training features available we prepare a
classification model by training the classifier using 2
supervised techniques KNN and SVM. Following
shows the model classifier used to predict the labels
of the images based on the classifier.
Model=fit[C/L] [Model type](X,Y);where C stands
for classification or regression, model type is any
supervised technique applied such as KNN or SVM,
X and Y represents the training features and labels
respectively.
face recognition high dimensionality feature
extraction can be used using CascadeObjectDetector
feature in Matlab.
IV. PERFORMANCE ANALYSIS
The experimentation is based on
calculating the processing time required to annotate
the image from the complete search based process
comparing the two supervised algorithms. There is a
considerable amount of change in time in both the
processing speed of both the supervised techniques.
Table 1 shows the results w.r.t to the time required
for each image annotation on different databases
with different number of actors available in given
dataset.
I. TABLE I
ML
technique
DB200
DB285
DB335
SVM
KNN
18-19
0.10-0.25
28-30
0.10-0.35
38-45
0.10-0.45
Table 1. Above table shows the processing time
required by each technique for returning the refined
labels of the images. We can see from the above
observations that time taken by KNN reduces
drastically as compared to SVM.
Graph 1. SVM carried out on 3 different databases
named as DB200, DB285 & DB335
where each database consists of 200, 285 & 335
images respectively based on the
readings
shown in Table1.
Label=predict (model, input) where input stands for
image query and model acquires the above
information. In order to increase the accuracy for
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International Journal of Engineering Trends and Technology (IJETT) – Volume 34 Number 2- April 2016
V. CONCLUSION
Graph 2. KNN carried out on 3 different databases
named as DB200, DB285 & DB335 where each
database consists of 200, 285 & 335 images
respectively based on the readings shown in Table1.
Database
N-5
N-15
N-25
N-30
DB200
100
99.8
99.3
98.38
DB285
99.98
99.72
98.45
98.3
DB335
99.9
99.65
98.4
98.18
Table 2. Above table shows the recognition
accuracy rate of n,
where n is the number of images thrown on
each database DB200,
DB285 & DB335 and gets the image
accuracy recognized in percentage.
This paper reviews the need of annotating
the images in today’s technologically growing world.
Moreover this paper also focuses on the accurate
labeling of those noisy or weakly labeled data
images through SBFA technique.
Further to refine the label quality for
annotating the images, KNN & SVM supervised
methods are used rather than unsupervised method
of learning. The 2 supervised techniques give drastic
change in processing time varying with different
databases. We recognize the web facial images
based on Histogram of oriented gradient features
called as HoG features based on feature extraction
and the labels are refined by predicting the model
based on the training labels acquired from the
weakly or noisy labels. Despite of refining the label
based on SBFA framework which is new
architecture, we also used the supervised method for
refining weak labels instead of unsupervised method
as mentioned in the previous base paper.
Though the work is an achievement in its own sense
yet it faces some limitations of refining the labels if
2 persons have the same label o share the same name.
Duplicity is little bit difficult to trace.
Evaluation of the same methodology on
larger databases can be considered as a future scope
of this project. Also different supervised as well as
semi-supervised algorithms can be used for label
refinement. Moreover different facial recognition
algorithms can be used to facial recognition to
improve the accuracy further.
REFERENCES
Graph 3. Based on the above observation, we have
represented the readings in the graphic format as
above where, n represents the number of images
treated with different databases for recognition. The
results are calculated on the basis of normal
probability function.
ISSN: 2231-5381
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International Journal of Engineering Trends and Technology (IJETT) – Volume 34 Number 2- April 2016
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