A hybrid content based image retrieval system using log-gabor filter banks

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 9, No. 1, February 2019, pp. 237~244
ISSN: 2088-8708, DOI: 10.11591/ijece.v9i1.pp237-244  237
Journal homepage: http://iaescore.com/journals/index.php/IJECE
A hybrid content based image retrieval system
using log-gabor filter banks
D. Madhavi1
, Khwaja Muinuddin Chisti Mohammed2
, N. Jyothi3
, M. Ramesh Patnaik4
1,2Department of ECE, GIT, GITAM Deemed to be University, India
3Department of EIE, GIT, GITAM Deemed to be University, India
4Department of Instrument Technology, AUCE, Andhra University, India
Article Info ABSTRACT
Article history:
Received Feb 14, 2018
Revised Sep 8, 2019
Accepted Sep 21, 2018
In this paper, a new efficient image retrieval system using sequential process
of three stages with filtering technique for the feature selection is proposed.
In the first stage the color features are extracted using color histogram
method and in the second stage the texture features are obtained using log-
Gabor filters and in the third stage shape features are extracted using shape
descriptors using polygonal fitting algorithm. The proposed log-Gabor filter
in the second stage has advantages of retrieving images over regular Gabor
filter for texture. It provides better representation of the images.
Experimental evaluation of the proposed system shows improved
performance in retrieval as compared to other existing systems in terms of
average precision and average recall.
Keywords:
Average Precision
Average Recall
Filtering techniques
Gabor Filter
Log-Gabor Copyright © 2019 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
D. Madhavi,
Department of ECE,GIT,GITAM Deemed to be University,
Visakhapatnam, Andhra Pradesh, 530045-India.
Email: dmadhavi336@gmail.com
1. INTRODUCTION
Content based Image retrieval is a challenging task to recover exact similar type of images from the
huge database. Many color, texture and shape feature extraction methods are available in literature. This
paper proposes a new Query by image content system using a log-Gabor filter. Sequence of feature extraction
in retrieval can be color, texture, shape or texture, shape color, and so on. It has been investigated that the
best sequence that can be used to improve the retrieval efficiency is color features first, secondly texture and
then the shape features [1]. The proposed image retrieval system is a hybrid method that is performed in three
stages. In first stage images are retrieved based on color features similarity followed by texture and shape
features in stage 2 and stage 3 respectively. Looking into methodology, in the first stage color features are
extracted using color histogram and the similar L images to the given query image are obtained using
similarity metric.
For texture feature extraction Gabor filters are generally used. However, the Gabor functions having
certain drawbacks relating to their frequency response representation. In this paper, texture feature extraction
using log-Gabor filters is proposed, which is the second stage of the proposed retrieval system. These filters
are theoretically superior to Gabor filters as they can better represent the frequency properties of natural
images. The L images that are obtained from stage 1 are given as the input data set for stage 2. The most
similar M images to the given query image are obtained from this stage 2. In stage 3, image retrieval is based
on shape feature extraction using curve polygonal fitting algorithm. The best N images to the query image are
obtained from this stage.
The main important features in image retrieval is texture. Different methods have been published by
many researchers in the area of image retrieval, especially they differ in the process of extracting the texture

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 9, No. 1, February 2019 : 237 - 244
238
feature. In [2] image retrieval process using a set of Gabor filters and the feature vector is constituted using
mean and standard deviation. Retrieval system using Gabor and Curvelet transform for retrieval of three
traditional paintings proposed in [3]. However it has not been used for combination of feature extraction.
In [4] comparision of different feature extraction techniques for CBIR using color texture and shape are
presented.Gabor filters are used for gender recognition from face images in [5]. To improve the retrieval
efficiency of Content Based Image Retrieval system color and texture features are combined [6] and three
moments of the color distribution are evaluated and texture features are extracted using Gabor filters.
The similarity is measured using Canberra distance.
Gabor filters suggested [7] for the representation of color texture using opponent and unichrome
features. In [8] image retrieval system using color, texture and shape features extraction with support vector
machine. Gabor filters for vehicle detection proposd in [9], the derived filters are evaluated using fitness
function based on support vector machines.Gabor filters triggered with different frequency, orientation and
scale [10] with minimization of energy computation for texture feature selection. In [11] optimized Gabor
filters are proposed for detection of defect in surface. Genetic algorithm optimized gray and color Gabor
filters in CBIR system design are considered in [12-13]. These research outputs shows that use of Gabor filter
for the texture extraction has most impact of reterival performance. However the Gabor filters are not having
zero mean and hence the frequency spectrum is not covered uniformly. So in this paper a log-Gabor filter is
considered for the texture feature extraction.
Literature also shows that log-Gabor filters have the advantage over the traditional Gabor in the area
of texture description and analysis. In [14] log-Gabor filters are used for automatic vehicle detection. Gabor
and log Gabor filters approach for texture feature attraction and the texture metrics are measured using
Kullback-Leibler and the Jensen-Shannon divergence [15]. Shail Kumari Shah et.al [16] proposed
recognition of three facial emotions-happy, neutral, surprise using Gabor, log-Gabor and principal
component analysis (PCA). The features of the face are extracted using Gabor and log-Gabor filters and the
dimensionality of the feature vector obtained is reduced by PCA. However the images of low resolution and
different rotations of head positions are not identified properly. Log-Gabor filters and support vector
machines are used for vehicle verification [17] on the road and their performance is compared with the
performance using traditional Gabor filters. In [18] Image retrieval system using color and shape features
with ESPRIT algorithm and Gabor filters are proposed. Gabor filters for different orientations and angles are
applied for image enhancement of finger print [19].
So, to take the advantage of the log-Gabor filter, in this paper we proposed log-Gabor filter bank for
the image retrieval in the second stage. The design of filter bank is based on different scale and orientation
parameters is discussed in the next section. This paper is organized as follows. In section 2 the functional
block diagram of the proposed image retrieval system is presented. In section 3 feature extraction of all three
stages are explained. Experimental analysis and results are shown in section 4. Conclusions are presented in
section 5.
2. PROPOSED METHOD
The functional block diagram of the proposed sequential three stage–hybrid image retrieval system
is shown in Figure 1. K database images are considered as input set. In stage 1, the best L relevant images to
the query image are obtained using HSV color space with color histogram. These L images are given as input
data set for stage 2. In this stage, a log-Gabor filter is used for texture feature extraction and best M images
are identified using minimum energy feature. These M database images are selected as a data set for stage 3.
In this stage, image retrieval is considered using shape feature extraction with polygonal curve fitting
algorithm. The best N images are extracted from this stage which are the best possible relevant images to the
given query image. We considered the relation in number of images that are extracted in each stage of the
proposed retrieval system as K>L>M>N.
Figure 1. Functional blocks of three stage - Hybrid system for image retrieval system

Int J Elec & Comp Eng ISSN: 2088-8708 
A Hybrid Content Based Image Retrieval System using Log-gabor Filter Banks (D. Madhavi)
239
3. FEATURE EXTRACTION METHODS
Features are extracted based on the color, texture and shape. In this section, we discuss the process
of feature extraction and the similarity measure used at each stage. The computational units in these stages
are shown in Figure 2. The extracted ‘i’ images from one stage are passed as input to the next successive
stages where ‘i’ takes values as K, L and M respectively at each stage. The following sub sections gives
details of extraction of these ‘i' images at each stage namely color, texture and shape.
Figure 2. Computational units of the proposed retrieval system
3.1. Retrieval process based on color features
Color features extraction is an important part in human recognition system. It is an easiest and
widely used method in CBIR. This feature is not variant with size and rotation of the picture. Different
methods used for color feature extraction are color histogram, correlogram. In this paper, the color
information is extracted from the large data set using color histogram. The color content extracted using HSV
color space and these values are quantized [20].
HSV histogram of n bins is used for the K input data set and query image for feature extraction [1].
The similarity between these is obtained by the distance measurement. The L images which are having
highest distance metric are considered and other are ignored, these L images are passed as input dataset to the
texture feature extraction stage.
If x and y are two histograms with size ‘n’ then the distance 𝐷 between the histograms x and y can
be given by:
𝐷 =
∑ min(𝑥[𝑗], 𝑦[𝑗])
min(|𝑥|, |𝑦|)
(1)
Where |𝑥| and |𝑦| are histogram magnitudes. This distance measurement is arranged in increasing order and
first L images are having best similar images to query image.
3.2. Rretrieval process based on log-gabor filter
Gabor filters are generally used for extracting texture features of images. However the high DC
component of these filters requires more number of filters for spectrum coverage. The frequency response of
these filters also symmetrically distributed at center frequency, which causes redundant data at low
frequencies. To overcome these limitations log Gabor filters are considered for texture feature extraction. The
symmetry of the log axis facilitates uniform coverage of the frequency spectrum and reduces the redundancy
at low frequencies. The log-Gabor filters having no DC component which allows an increase in the

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bandwidth, and hence fewer filters are required to cover the same spectrum as compared to original Gabor
filters.
Filter over comes angular wrap around problem, for which it consider cosine and sine difference as
shown in (2) and (3).
𝐷𝑠 = sin 𝜃 ∗ cos ∅ − cos 𝜃 ∗ sin ∅ (2)
𝐷𝑐 = 𝑐𝑜𝑠 𝜃 ∗ 𝑐𝑜𝑠 ∅ +𝑠𝑖𝑛 𝜃 ∗ 𝑠𝑖𝑛 ∅ (3)
Where 𝜃 = tan ( ) with x and y as allowed size of the filter, radius = 𝑥 + 𝑦 , orientation ∅= 0 to
(2n-1)𝜋/n, with n as number of orientations.
𝐴𝑑 = tan (4)
The log Gabor filter introduced by Field, and its frequency domain representation is given by:
𝐺(𝑤) = 𝑒
( )
( ( )) (5)
Here 𝑤 = 1/λ , where ‘λ’ changes with the specific scale, here the resultant value of the ‘λ’ selected as
wavelength * s; where s is a scaling factor between different filters.
Where 𝜎 is standard deviation of the angular Gaussian function and 𝑤 is its center frequency. The
bandwidth of the filter is effected by the variable 𝜎.
By considering not only the particular frequency but also the orientation factor, the two dimensional
log- Gabor filter is given by (6).
𝐺(𝑓, 𝜃) = 𝐺(𝑤) . 𝑒 ∅
(6)
Here 𝜎∅ =
∗
, k= interval between filter orientations/𝜎
The texture features are obtained by taking a log-Gabor filter which is specified by (5) for different
scales and orientations and are shown in Figure 3. A total of 12 filter banks are obtained by considering three
scales and four orentations. The energy functions are computed by convolving these filters with the query
and the each data set images. These energy functions are used as feature set for matching the query image
with the data set. The similarity between the energy features are computed to choose the best matched
images. The best M images that with most relavent energies to the query image are obtained from this stage.

241
Figure 3. Bank of log- Gabor filters with scale =3 and orientation=4 (A total of 12 filters)
3.3. Retrieval process based on shape features
The best M images that are obtained from the previous stage are considered as the input database for
this third stage. Here the Fourier representation of the shape signature functions are computed and the outer
boundary shape is developed using polygon fitting algorithm [20]. The minimal distance between the pixels
is considered to be one unit. The magnitude of shape descriptors for query image and all the input data set of
stage 3 are computed and Euclidean distance metric is calculated between query image and all the input data
set using (7).
𝑑 = 𝑓 − 𝑓 (7)
Where f the characteristic vector of is required image and f is the characteristic vectors of all input data set
images of stage 3.
4. RESULTS AND ANALYSIS
The experimental analysis of the proposed log-Gabor filter based image retrieval system is analyzed
by using Wang database [21] by taking 1000 images of 10 classes. An elephant image in the dataset is
considered as query image and is shown in Figure 4. The values of L, M and N are taken as 8, 6 and 3
respectively.The best 8 images that are relevant to query image are obtained in stage 1 and is given as input
set for stage 2. From stage 2, six best possible images are obtained and it is applied as input data set for
stage 3. Finally from stage 3 best 3 images are extracted. The retrieval results of each stage are shown in
Figure 5, 6, 7.
The efficiency of the proposed retrieval scheme is tested by the performance metrics Average
Precision (A.P) and Average Recall (A.R) which are defined as:
𝐴. 𝑃 =
𝑁𝑜. 𝑜𝑓 𝑟𝑒𝑙𝑒𝑣𝑎𝑛𝑡 𝑖𝑚𝑎𝑔𝑒𝑠 𝑟𝑒𝑡𝑟𝑖𝑒𝑣𝑒𝑑
𝑁𝑜. 𝑜𝑓 𝑖𝑚𝑎𝑔𝑒𝑠 𝑟𝑒𝑡𝑟𝑖𝑒𝑣𝑒𝑑
(8)
𝐴. 𝑅 =
𝑁𝑜. 𝑜𝑓 𝑟𝑒𝑙𝑒𝑣𝑎𝑛𝑡 𝑖𝑚𝑎𝑔𝑒𝑠 𝑟𝑒𝑡𝑟𝑖𝑒𝑣𝑒𝑑
𝑁𝑜. 𝑜𝑓 𝑟𝑒𝑙𝑒𝑣𝑎𝑛𝑡 𝑖𝑚𝑎𝑔𝑒𝑠 𝑖𝑛 𝑡ℎ𝑒 𝑑𝑎𝑡𝑎 𝑠𝑒𝑡
(9)

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Figure 4. Query image
Figure 5. Stage 1 retrieved images with L=8
Figure 6. Stage 2 retrieved images with M=6
Figure 7. Stage 3 retrieved images with N=3
By using retrieval outcomes of each of these images, the performance of the proposed log- Gabor
filter based CBIR system is compared with the Nishant model [1] and Chuen model [22]. The Average
Precision and Average Recall are compared by varying the number of retrieved images from 20 to 100 and
are shown in Figure 8 and Figure 9. It is observed that the proposed log-Gabor filter based system shows
improved average Precision and average Recall compared to other two models.

243
Figure 8. Comparison of average precision of the
proposed log-gabor filter based on using of CBIR
system and Nishant Shrivastava model,
and Chuen model
Figure 9. Comparison of average recall of the
proposed log-gabor filter based on using of CBIR and
Nishant Shrivastava model, and Chuen model
5. CONCLUSION
Content based image retrieval is a challenging task for obtaining similar images from a large
database. In this paper a sequential process of color, texture and shape feature analysis of a CBIR system has
been proposed. The proposed system makes use of the advantages of log- Gabor filter over the traditional
Gabor filter, which has shown a visible difference in terms of retrieval performance metirs. The effectiveness
of the proposed system verified using performance metrics of average precision and average recall. The
experiments conducted in this paper confirm that the proposed CBIR system using log-Gabor filters for
texture feature extraction significantly outperforms the original Gabor filter for content based image retrieval.
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