A Novel Method for Content Based Image Retrieval using Local Features and SVM Classifier

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 04 Issue: 07 | July -2017 www.irjet.net p-ISSN: 2395-0072
A novel method for Content Based Image retrieval using Local
features and SVM classifier
Mr. Yogen Mahesh Lohite1, Prof. Sushant J. Pawar2
1Department of Electronics and Telecommunication Sandip Inst. of Tech. & Research Centre
Nasik, India
2Department of Electronics and Telecommunication Sandip Inst. of Tech. & Research Centre
Nasik, India
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Abstract - Retrieving images from the large amount of
database based on their content are called content based image
retrieval. It is a basic requirement of retrieve the relevant
information from huge amount of image database according to
query image with better system performance. With increasing
volume of digital data, search and retrieval of relevant images
from large datasets in accurate and efficient way is a
challenging problem. Color texture and edge feature of image is
most widely used feature to analyze the image in the CBIR. In
this paper we present a novel approach for retrieval of images
based on this features and have also optimized the results using
the SVM classifier. The proposed system is implemented in
matlab and efficiency of the is calculated on the parameters
like accuracy, sensitivity, specificity, error rate and retrieval
time. The results shows that the proposed system outperforms
well than other technique.
Key Words: Content Based Image Retrieval; TextBased
Image Retrieval ; Feature Extraction; Manhattan
distance, SVM,
1.INTRODUCTION
Image retrieval in general and content based image retrieval
in particular are well-known research fields in information
management. The large numbers of images has created
increasing challenges to computer systems to search &
retrieve relevant images efficiently[1]. Researchers are
gaining more interest in CBIR as it is one of the hot image
processing field which is having big range to work out the
novel ideas that will produce the promising results. Core
phases of CBIR where the research contribution is desired,
are feature extraction based on image contents, Similarity
measures used for comparison and the performance
evaluation using various parameters. [2]
The main consideration of image retrieval is the structure of
images in image database, Here, the database images are
stored in structured manner. The scenario of CBIR is mainly
indexing images in image database and retrieval. Firstly,
using multiple features generates the feature vectors and
those are accordingly stored in an index correlated to the
database images. And then, based on the similarity
measure between database images and query image the
relevant images will be retrieved. [3]
Initially, Content-Based Image Retrieval (CBIR) systems were
introduced to address the problems associated with text-
based image retrieval. CBIR is a set of methods for retrieving
semantically-relevant images from an image database based
on automatically-derived image features. The main goal of
CBIR is efficiency during image indexing and retrieval,
thereby reducing the need for human intervention in the
indexing process. In other words, visual contents are used in
CBIR to search images from large scale image databases
based on users’ interests. It becomes an active and fast
advancing research area. Image content may include both
visual and semantic content. Retrieving images on the basis of
automatically-derived features such as color, texture and
shape is the basic way of CBIR. These techniques includes
several areas such as image segmentation, image feature
extraction, representation, mapping of features to semantics,
storage and indexing, image similarity-distance measurement
and retrieval which makes CBIR system development as a
challenging task.
Implementation of a CBIR system using one content feature
doesn’t give sufficient retrieval accuracy [4]. To overcome
this problem, we combine multiple features for the image like
color, texture, & edge. The objective is to work on collection
of images & retrieve similar images based on features in
response to pictorial queries. Despite the vast amount of
review work exists for image retrieval methods but after
assaying the work, lack of systematic literature review &
performance evaluation of existing techniques for CBIR is
realized. It will explore the research gaps & statistical
knowledge for future researches. Traditionally, text based
image retrieval also known as concept based image retrieval
is the most common retrieval system, where the search is
based on annotation of images. The term CBIR was coined by
Kato in 1992 in his research article “Database architecture for
content base image retrieval”, for the
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1741

automatic retrieval of the images from a database based
on the color and the shape [5].
CBIR is an interface between a high level system (the
human brain) and a low level system (a computer). The
human brain is capable of performing complex visual
perception, but is limited in speed while a computer is
capable of restricted visual capabilities at much higher
speeds. In a CBIR, visual image content is represented in
form of image features, which are extracted automatically
and there is no manual intervention, thus eliminating the
dependency on humans in the feature extraction stage.
These automated feature extraction approaches are
computationally expensive, difficult and tend to be domain
specific . In this paper we present a novel approach for
retrieval of images based on this features and have also
optimized the results using the SVM classifier.
texture and color attributes are computed in a way that
model the Human Vision System (HSV) [11].
The Texture semantics is retrieved using Gabor wavelets.
Shape feature is extracted using Gradient Vector Flow fields.
It shows an accuracy of 60.7% by the authors in [12] but the
disadvantage is that it has very low accuracy. In [13] the
authors proposes a method which uses Color features of an
image to form a feature vector. These features are then used
by machine learning classifiers to classify the images, but
Texture and shape features are not considered.
2.1 CBIR Architecture
The basic fundamentals of content based image retrieval
are divided into three parts feature extraction; feature
matching and retrieval system design. The proper
organization of the generated large amount of images is
also needed in CBIR system.2. LITERATURE REVIEW
(CBIR) is a method that is used to look at image features like
(color, shape, texture) to find a query image from database.
The difficulties of CBIR lie in reducing the differences of
contents based feature and the semantic based features. This
problem in giving effective retrieval images and channelize
the researchers to use (CBIR) system ,to take global color and
texture features to achieve, the good retrieval, where others
used local color and texture features[6].
The method in [7] presented the holistic representation of
spatial envelop with a very low dimensionality for making the
incident image. This approach presented an outstanding
result in the scene categorization. The method in [8]
proposed a modern approach for image classification with the
open field design and the concept of over-completeness
methodology to achieve a preferable result. As reported in
[8], this method achieved the best classification performance
with much lower feature spatiality compared to that of the
former schemes in image classification task.
Tiwari et al developed a CBIR system [PATSEEK] for US
based patent database as a patent always consists of an
image along with textual information. For similarity search
[9] the user need to enter keywords along with the query
image that might appear in the text of patents. Krishnan et
al developed CBIR based on color, based on the rife colors
in the foreground image which gives only the semantics of
the image. Dominant color identification by using
foreground objects alone is able to retrieve number of
similar images considering the foreground color
irrespective of size. Higher average precision and recall
rates compared to the traditional Dominant Color method
were obtained successfully [10].
In another system the image is represented by a Fuzzy
Attributed Relational Graph (FARG) that describes each
object in the image, its attributes and spatial relation. The
Fig -1: Basic Block diagram of CBIR
The CBIR system has following steps:
1. Create a database: Store images in a database to
prepare own database for testing purpose or use
inbuilt databases.
2. Input Query Image: Input query image for which
similar images for database are needed to be
retrieved.
3. Feature Extraction: Extracting the important
features of database images and query image
based on various image features like color,
texture, edge features etc.
4. Feature Matching: Measure similarity between
query image and stored database images based on
Manhattan distance, Euclidean distance, chisquare
distance etc. is checked and the features which are
closer to the query image features the corresponding
image of that features are retrieved.
5. Evaluate Results: Based on certain parameters
like sensitivity, specificity, accuracy rate evaluate
retrieved images.

3. Proposed Approach
3.1 Feature Extraction
The proposed system design is given in two phases.
Training Phase: Feature extraction applied for image
database is a backend process which is independent from
user extraction.The extracted features are smaller than
actual image and then they are stored as feature database
in the form of matrix for similarity measures later on. The
collection of feature vectors is termed as feature database
of the images in the database.
Testing Phase: This phase is also known as front end
starts when user gives a specific query request by giving an
example image. Then, features of query image are also
extracted in same manner as database image features are
extracted and stored as a feature vector. Then similarity is
measured based on chosen distance metrics and based on
least distance set of most similar images is obtained as result.
HSV
During this step following actions are done, Color Space
Conversion, Color Quantization and Compute Histogram. In
color space conversion, Translate the representation of all
colors in each image from the RGB space to the HSV space. A
color histogram is a representation of the distribution of
colors in an image.Each component is quantized with non-
equal intervals: H: 8 bins; S: 3 bins and V: 3 bins. Finally we
concatenate 8X3X3 histogram and get 72-dimensional vector.
Color Moment
Color moments are measures that can be used
differentiate images based on their features of color.The most
important moments are Mean, Standard deviation and
Skewness. The first order (mean), the second (standard
deviation) and the third order (skewness) color moments
have been proved to be efficient and effective in representing
color distributions of images. In RGB, each channel will be 3-
values vector. In total we have 3 x 3 = 9 values for each image.
3.2 Proposed Algorithm
Image Database
The database of collection of 300 images is being used.
Images are divided into different categories like horses,
aero planes, cars, roses, monuments, players etc. of JPEG
format and each category contains similar type of images.
To reduce the number of calculations at run-time, every
image in the database should be pre-computed.The
following algorithm will train the database and store
extracted features as feature database for further use.
Algorithm for Training Phase
Phase1 (Training Phase): The proposed algorithm for
feature extraction and storage is:
OUTPUT: Feature based representation of database
images
Step 1: Read an Image from the database
Step 2: Quantize the image into Hue, Saturation and
Value (HSV) into 8x3x3 value.
Step 3: Compute the HSV Histogram.
Step 4: Extract first 3 color moments from each Red,
Green and Blue Planes of image.
Step 5: Convert image to Gray Scale image.
Step 6: Apply
Gabor Wavelet (no. of scales = 4 and no. oforientation = 6)
to calculate mean squared energy and mean amplitude.
Step 7: Apply Wavelet moment to calculate first 2
moments of wavelet coefficients i.e. mean coefficient
and standard variation coefficient.
Step 8: Apply edge gradient using sobel edge detection
to calculate gradient magnitude or edge strength.
Step 9: Apply 1 to 7 on all images stored in a database
and store features as feature database.
Algorithm for Testing Phase
Phase2 (Testing Phase): The proposed algorithm for
image retrieval from storage is:
INPUT: Query image.
OUTPUT: Similar images retrieved from the database
Step 1: Load the query image.
Step 2: Extract features for query image (As given in
Training
algorithm steps 2 to 8).
Step 3: Create the feature vector by combining selected
features, that is, HSV histogram, color moment, Gabor
wavelet, Wavelet moment and Edge gradient
Step 4: Compute the matches between feature vector
of query image and feature vector of each of the images
in the database using distance metrices.
Step 5: Retrieve the top n images based on the order
of minimum distance using all distance metrices.
INPUT: RGB images from the database

AC = Sensitivity + Specificity/2
Retrieval Score: A retrieval score was computed for each
query, the system returned the n closest images to the
query, including the query image itself .Its formula is:
Retrieval Score =100 × [1− (mismatches/n)] %
Retrieval Time: It gives total time after giving source image
to get similar images from database. It is measured in
seconds. Error Rate: It gives error value occur in retrieval
process.Its formula is:
Error Rate = 1 - Accuracy
4. RESULTS
Qualitative Evaluation: To perform evaluation and
comparison studies of experiments are set up in MATLAB
8.10.604 (R2013a) on i3 Processor and proposed system
is tested on various parameters.
Fig -2: Flow Chart of Proposed System
3.3 Evaluation Metrics
Traditional framework of evaluation consists of
Sensitivity, Specificity, Accuracy, and Error Rate.
True Positive: This term tells us number of matched images
which are correctly identified.
False Negative: It is reverse of true positive i.e. it gives
number of matched images which are not correctly
identified. It considers matched images as not matched.
True Negative: It indicates number of images which are not
matched and those are correctly identified.
False Positive: It gives us number of not matched images
which are not correctly identified. It consider not matched
images as matched images
Sensitivity: By this parameter we can find the value of number
of images is correctly matched. It can be calculated by
Sensitivity = TP / (TP+FN)
Specificity: It gives us the value of number of images which
Are not matched. It is calculated by a formula given as:
Specificity = TN / (TN+FP)
Fig -3: GUI without selecting any database or test image
Fig -4: Image Dataset
Accuracy: It simply provides us the average of sensitivity and
specificity and is calculated as:

Fig -5: Test Image and Similar Images retrieved
by manhattan
by Chebychev
by Cityblock
Quantitative Evaluation: It is the systematic computation
and empirical investigation of statistical metrics for CBIR
Table -1: Sample Table format
Class Of Image Name Of Class
C 1 Africa
C 2 Beach
C 3 Monuments
C 4 Buses
C 5 Dinosaurs
C 6 Elephants
C 7 Flowers
C 8 Horses
C 9 Mountains
C 10 Food
TABLE -2. Experimental results for parameters
of Proposed cbir system
Class of Accuracy Sensitivity Specifi Error Retrieval
Image city Rate Time
C 1 86.12% 0.89 0.79 0.14 1.052310
sec
C 2 82.86% 0.67 0.78 0.17 1.064574
sec
C 3 83.67% 0.76 0.81 0.16 1.009583
sec
C 4 83.27% 0.76 0.74 0.17 1.044011
sec
C 5 83.27% 0.85 0.70 0.17 1.016916
sec
C 6 82.45% 0.85 0.57 0.18 1.045035
sec
C 7 84.29% 0.78 0.64 0.16 1.116216
sec
C 8 85.31% 0.76 0.74 0.15 0.992197
sec
C 9 84.69% 0.93 0.75 0.15 1.029994
sec
C 10 86.33% 0.89 0.69 0.14 1.071573
sec
Average 84.23% 0.81 0.72 0.15 1.044
TABLE -3. Experimental results for comparison
of Different similarity measures
Fig -7: Test Image and Similar Images retrieved by
Eucledian

3. CONCLUSIONS
An novel method is proposed for retrieval based on
combination of color, texture & edge features of image with
svm classifier for optimization of results. Performance
evaluation of proposed technique is done using parameters
like Sensitivity, Specificity, Retrieval score, Error rate and
Accuracy. Experimental results on 10 categories of images
each with 50 images demonstrate that proposed technique
along with 11 distance parameters as similarity measure with
average accuracy 0.844 outperforms other techniques.
Our sincere thanks go to SITRC for providing a strong
platform to develop our skill and capabilities. We would
like to thanks all those who directly or indirectly help us in
presenting the paper. We hereby take this opportunity to
express our heartfelt gratitude towards the people whose
help is very useful to complete our project. We would like
to express our heartfelt thanks to my guide Prof. Sushant J.
Pawar whose experienced guidance became very valuable
for us.
REFERENCES
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ACKNOWLEDGEMENT

A Novel Method for Content Based Image Retrieval using Local Features and SVM Classifier

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