Feature integration for image information retrieval using image mining techniques

International Journal of Computer Engineering COMPUTER (IJCET), ISSN 0976 –
INTERNATIONAL JOURNAL OF and Technology ENGINEERING
6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME
& TECHNOLOGY (IJCET)
ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 3, Issue 3, October - December (2012), pp. 273-281
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FEATURE INTEGRATION FOR IMAGE INFORMATION RETRIEVAL
USING IMAGE MINING TECHNIQUES

Madhubala Myneni
Professor, Dept. of CSE , Mahaveer Institute of Science and Technology, Bandlaguda,
vyasapuri, kesavagiri Post Hyderabad-5
baladandamudi@gmail.com

Dr.M.Seetha
Professor, Dept. of CSE, G.Narayanamma Institute of Technology & Science for
Women, Shaikpet, Hyderabad
smaddala2000@yahoomail.com

ABSTRACT
In this image information retrieval system the feature extraction process is analyzed and a new
set of integrated features are proposed using image mining techniques. The content of an image
can be expressed in terms of low level features as color, texture and shape. The primitive features
are extracted and compared with data set by using various feature extraction algorithms like
color histograms, wavelet decomposition and canny algorithms. This paper emphasizes on
feature extraction algorithms and performance comparison among all algorithms. The system
uses image mining techniques for converting low level semantic characteristics into high level
Features, which are integrated in all combinations of primitive features. The feature integration
has restricted to three different methodologies of feature Integration: color and texture, color and
shape, texture and shape. It is ascertained that the performance is superior when the image
retrieval based on the super set of integrated features, and better results than primitive set.

Keywords-Feature Extraction, Feature Integration, Image Information Retrieval

1. INTRODUCTION

Image Information Retrieval aims to provide an effective and efficient tool for managing large
image databases. Image retrieval is one of the most exciting and fastest growing research areas in
the field of medical imaging [2]. The goal of CBIR is to retrieve images from a database that are
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International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –

similar to an image placed as a query. In CBIR, for each image in the database, features are
extracted and compared to the features of the query image. A CBIR method typically converts an
image into a feature vector representation and matches with the images in the database to find
out the most similar images. In various studies different databases have been to compare the
study.
Content-based image retrieval (CBIR) has been an active research topic since 1990s. Such
systems index images using their visual characteristics, such as color, texture and shape, which
can be extracted from image itself automatically. They can accept an image example as query,
and return a list of images that are similar to the query example in appearance. To extract the
primitive features of a query image and compare them to those of database images. The image
features under consideration were colour, texture and shape. Thus, using matching and
comparison algorithms, the colour, texture and shape features of one image are compared and
matched to the corresponding features of another image. In the end, these metrics are performed
one after another, so as to retrieve database images that are similar to the query. The similarity
between features was to be calculated using algorithms used by well known CBIR systems such
as IBM's QBIC. For each specific feature there is a specific algorithm for extraction and another
for matching.
The integration of structure features, which are particularly suitable for the retrieval of manmade
objects, and color and texture features, which are geared towards the retrieval of natural images
in general. Specifically, we restrict our attention to three different methodologies of feature
Integration: color and texture, color and shape, texture and shape results in better performance
than using shape, color, and texture individually. Retrieving the relevant images in image
retrieval system is a three step process: To extract the relevant primitive features of color, texture
and shape, To design the filter for each feature, To generate the integrated features and perform
the comparison.

2. FEATURE EXTRACTION

Feature Extraction is the process of creating a representation, or a transformation from the
original data. The images have the primitive features like color, texture, shape, edge, shadows,
temporal details etc. The features that were most promising were color, texture and shape/edge.
The reasons are color can occur in limited range of set. Hence the picture elements can be
compared to these spectra. Texture is defined as a neighborhood feature as a region or a block.
The variation of each pixel with respect to its neighboring pixels defines texture. Hence the
textural details of similar regions can be compared with a texture template. shape/edge is simply
a large change in frequency. The three feature descriptors mainly used most frequently during
feature extraction are color, texture and shape.
Colors are defined in three dimensional color spaces. These could either be RGB (Red, Green,
and Blue), HSV (Hue, Saturation, and Value) or HSB (Hue, Saturation, and Brightness). The last
two are dependent on the human perception of hue, saturation, and brightness. Most image
formats such as JPEG, BMP, GIF, use the RGB color space to store information. The RGB
color space is defined as a unit cube with red, green, and blue axes.
Texture is that innate property of all surfaces that describes visual patterns, each having
properties of homogeneity. It contains important information about the structural arrangement of
the surface, such as; clouds, leaves, bricks, fabric, etc. It also describes the relationship of the
surface to the surrounding environment. In short, it is a feature that describes the distinctive

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October-December

physical composition of a surface.The most popular statistical representations of texture are: Co-
surface.The Co
occurrence Matrix, Tamura Texture and Wavelet Transform.
The classification of all methods among the two other approaches: structural and statistical.
The statistical point of view, an image is a complicated pattern on which statistics can be
he
obtained to characterize these patterns. The techniques used within the family of statistical
approaches make use of the intensity values of each pixel in an image, and apply various
s
statistical formulae to the pixels in order to calculate feature descriptors. Texture feature
descriptors, extracted through the use of statistical methods, can be classified into two categories
into
according to the order of the statistical function that is utilized: First-Order Texture Features and
Order
Second Order Texture Features. First Order Texture Features are extracted exclusively from the
information provided by the intensity histograms, thus yield no information about the locations
histograms,
of the pixels. Another term used for FirstFirst-Order Texture Features is Grey Level Distribution
Moments.
Shape may be defined as the characteristic surface configuration of an object; an outline or
contour. It permits an object to be distinguished from its surroundings by its outline. Shape
ur.
representations are two categories Boundary
Boundary-based and Region-based.
Canny Edge detection is an optimal smoothing filter given the criteria of detection, localization
and minimizing multiple responses to a single edge. This method showed that the optimal filter
nd
given these assumptions is a sum of four exponential terms. It also showed that this filter can be
well approximated by first-order derivatives of Gaussians. Canny also introduced the notion of
order Canny
non-maximum suppression, which means that given the pre smoothing filters, edge points are
maximum pre-smoothing
defined as points where the gradient magnitude assumes a local maximum in the gradient
direction.

Sobel edge detection operations are performed on the data and the processed data is sent back to the
computer. The transfer of data is done using parallel port interface operating in bidirectional mode. For
estimating image gradients from the input image or a smoothed version of it, different gradient operators
different
can be applied. The simplest approach is to use central differences:

corresponding to the application of the following filter masks to the image data:

The well-known and earlier Sobel operator is based on the following filters:
known

Given such estimates of first- order derivatives, the gradient magnitude is then computed as:
derivatives,

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October-December

while the gradient orientation can be estimated as

3. IMAGE INFORMATION RETRIEVAL
In general all Image retrieval algorithms are based on Image Primitive features like color, texture
ge colo
and shape. In this paper, the proposal of Comb Combinational features is specified to give good
performance. On each feature more efficient algorithms are used to retrieve the information from
data set.
3.1. Image Retrieval based on Color
.1.
In this paper color based image retrieval has performed in two steps. First for extracting color
ps.
feature information Global color histograms has used. To quantize the colors, number of bins are
20. Second step is calculating the distance between bins by using Quadratic distance algorithm.
The results are the distance from zero the less similar the images are in color similarity.
m
3.1.2 Color Histograms
Global color histograms extract the color features of images. To quantize the number of bins are
20. This means that colors that are distinct yet similar are assigned to the same bin reducing the
the
number of bins from 256 to 20. This obviously decreases the information content of images, but
decreases the time in calculating the color distance between two histograms. On the other hand
he
keeping the number of bins at 256 gives a more accurate result in terms of color distance. Later
on we went back to 256 bins due to some inconsistencies obtained in the color distances between
images. There hasn't been any evidence to show which color space generates the best retrieval
results, thus the use of this color space did not restrict us an anyway.
hus
3.1.3. Quadratic Distance Algorithm
The equation we used in deriving the distance between two color histograms is the quadratic
distance metric:
t
(
d 2 ( Q , I ) = HQ − H I ) A( H Q − HI )
The equation consists of three terms. The derivation of each of these terms will be explained in
the following sections. The first term consists of the difference between two color histograms; or
colo
more precisely the difference in the number of pixels in each bin. This term is obviously a vector
obviously
since it consists of one row. The number of columns in this vector is the number of bins in a
histogram. The third term is the transpose of that vector. The middle term is the similarity
matrix. The final result d represents the colo distance between two images. The closer the
color tance
distance is to zero the closer the images are in color similarity. The further the distance from zero
the less similar the images are in color similarity.

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3.1.4. Similarity Matrix
As can be seen from the color histograms of two images Q and I, the color patterns observed in
the color bar are totally different. A simple distance metric involving the subtraction of the
number of pixels in the 1st bin of one histogram from the 1st bin of another histogram and so on is
not adequate. This metric is referred to as a Minkowski-Form Distance Metric, which only
compares the “same bins between color histograms “.
This is the main reason for using the quadratic distance metric. More precisely it is the middle
term of the equation or similarity matrix A that helps us overcome the problem of different color
maps. The similarity matrix is obtained through a complex algorithm:
1
 2 2 2
2
(
 v − vi
 q
) ( ( )
+ sq cos hq − si cos(hi ) ) + (s q ( )
sin hq − si sin(hi ) ) 

aq ,i = 1 −
5
Which basically compares one colour bin of HQ with all those of HI to try and find out which
color bin is the most similar? This is continued until we have compared all the color bins of HQ.
In doing so we get an N x N matrix, N representing the number of bins. What indicates whether
the color patterns of two histograms are similar is the diagonal of the matrix, shown below. If the
diagonal entirely consists of one’s then the color patterns are identical. The further the numbers
in the diagonal are from one, the less similar the color patterns are. Thus the problem of
comparing totally unrelated bins is solved.
3.2. Image Retrieval based on Texture
In this paper texture based image retrieval has performed in three steps. First for extracting
statistical texture information Pyramid-structured wavelet transform has used. This
decomposition has been done in five levels. Second step is calculating energy levels on each
decomposition level. Third step is calculating Euclidian distance between query image and
database images. The top most five images from the list are displayed as query result.
3.2.1. Pyramid-Structured Wavelet Transform
This transformation technique is suitable for signals consisting of components with information
concentrated in lower frequency channels. Due to the innate image properties that allows for
most information to exist in lower sub-bands, the pyramid-structured wavelet transform is highly
sufficient.
Using the pyramid-structured wavelet transform, the texture image is decomposed into four sub
images, in low-low, low-high, high-low and high-high sub-bands. At this point, the energy level
of each sub-band is calculated .This is first level decomposition. Using the low-low sub-band for
further decomposition, we reached fifth level decomposition, for our project. The reason for this
is the basic assumption that the energy of an image is concentrated in the low-low band. For this
reason the wavelet function used is the Daubechies wavelet.
For this reason, it is mostly suitable for signals consisting of components with information
concentrated in lower frequency channels. Due to the innate image properties that allows for
most information to exist in lower sub-bands, the pyramid-structured wavelet transform is highly
sufficient.
3.2.2. Energy Level
Energy Level Algorithm:
• Decompose the image into four sub-images
• Calculate the energy of all decomposed images at the same scale, using [2]:

277

m n
1
E =
MN
∑∑ X (i , j )
i =1 j =1

where M and N are the dimensions of the image, and X is the intensity of the pixel located at
row i and column j in the image map.
• Repeat from step 1 for the low-low sub-band image, until index ind is equal to 5.
Increment ind.
Using the above algorithm, the energy levels of the sub-bands were calculated, and further
decomposition of the low-low sub-band image. This is repeated five times, to reach fifth level
decomposition. These energy level values are stored to be used in the Euclidean distance
algorithm.

3.2.3. Euclidean Distance
Euclidean Distance Algorithm:
- Decompose query image.
- Get the energies of the first dominant k channels.
- For image i in the database obtain the k energies.
- Calculate the Euclidean distance between the two sets of energies, using [2]:
k

∑ (x
2
Di =
k =1
k − yi , k )
- Increment i. Repeat from step 3.
Using the above algorithm, the query image is searched for in the image database. The Euclidean
distance is calculated between the query image and every image in the database. This process is
repeated until all the images in the database have been compared with the query image. Upon
completion of the Euclidean distance algorithm, we have an array of Euclidean distances, which
is then sorted. The five topmost images are then displayed as a result of the texture search.

3.3. Image Retrieval based on Shape
In this paper shape based image retrieval has performed in two steps. First for extracting edge
feature canny edge detection algorithm and sobel edge detection algorithm are used. Second step
is calculating Euclidian distance between query image and database images. The top most five
images from the list are displayed as query result.
3.3.1. Canny Edge Detection Algorithm
The Canny edge detection was developed by John F. Canny in 1986 and uses a multi-stage
algorithm to detect a wide range of edges in images. Stages of the Canny algorithm
- Noise reduction
- Finding the intensity gradient of the image
- Non-maximum suppression
- Tracing edges through the image and hysteresis threshold
- Differential geometric formulation of the Canny edge detector
-

278


3.3.2. Sobel Edge detection:
The implementation of image edge detection on hardware is important for increasing processing
speed of image processing systems. Edge information for a particular pixel is obtained by
exploring the brightness of pixels in the neighborhood of that pixel. Measuring the relative
brightness of pixels in a neighborhood is mathematically analogous to calculating the derivative
of brightness. The pixel information extracted is transferred from the computer to the field
programmable gate array device. sobel edge detection operations are performed on the data and
the processed data is sent back to the computer. The transfer of data is done using parallel port
interface operating in bidirectional mode.
The algorithm mentioned in the previous section, for finding the Euclidean Distance between
two images is used to compute the similarity between the images.
4. Feature Integration
All the extracted features are integrated to get the final extracted image as result. Every block
has a similarity measure for each of the features. Hence after the feature extraction process each
instance (block) is a sequence of 1s (YES) and 0s (NO) of length equal to the number of
features extracted. Combining these extracted features is synonymous to forming rules. One rule
that combines the three features is color & edge | textures, which means color AND edge OR
texture.
Depending on the features used and the domain, the rules vary. If a particular feature is very
accurate for a domain, then the rule will assign the class label as YES (1) (1 in the table on the
left). For those instances when I Class is not certain the class label is 2. This denotes uncertain
regions that may or may not be existed. The same rule used during the training phase is also
used in the testing phase.

Table1: Rules for Feature Integration
Rule Name Color Texture Edge
Class-1 1 0 1

Class-2 0 0 0

Class-3 1 1 0

Class-4 1 0 0

If there are 3 features, for example, the above table shows a part of a set of rules that could be
used. The first and third rules say that color along with texture or edge conclusively determines
that query image is present in that block. The second rule says that when none of the features is
1 then query image is absent for sure. Fourth rule states that color on its own is uncertain in
determining the presence of query image.

279


5. RESULTS AND OBSERVATIONS

The image database has used to retrieve the relevant images based on query image. The test
image database contains 200 images on various categories. Image retrieval has performed on
combinational feature set of primitive features like color, texture and shape.
Based on commonly used performance measures in information retrieval, two statistical
measures were computed to assess system performance namely Recall and Precision. For good
retrieval system ideal values for recall is 1 and precision is of low value.
The following table will give the performance evaluation of Image retrieval based on proposed
Integrated feature of color and Texture,Integrated Feature of Color and shape,Texture and shape.

Table 2. comparitive analysis on three categories of integrated features
Qery image
categories
Color and Texture Color and shape Texture and Shape
Precision Recall Precision Recall Precision Recall
Glass 0.444 1 0.833 1 1 0.8
Mobile 0.666 0.857 0.833 0.714 0.75 0.428
Apple 0.555 1 0.833 0.8 0.75 0.6
Narcissi Lilly 0.667 1 1 1 1 0.667
Horse 0.667 1 0.833 0.833 1 0.667
Cow 0.444 0.8 0.667 0.8 1 0.8
SunFlower 0.667 0.75 0.83 0.625 0.75 0.375

6. CONCLUSION AND FUTURE ENHANCEMENTS

This paper elucidates the potentials of extraction of features of the image using color, texture and
shape for retrieving the images from the specific image databases. The images are retrieved from
the given database of images by giving the query image using color, texture and shape features.
These results are based on various digital images of dataset. The performance of the image
retrieval was assessed using the parameters recall rate and precision. It was ascertained that the
recall rate and precision are high when the image retrieval was based on the Feature Integration
on all the three features the color, texture and shape than primitive features alone. This work can
be extended further on huge data bases for retrieving relevant image. This can be extended for
pixel clustering to obtain objects using different combination of weight for color and texture and
shape features. It can maximize the performance by choosing the best combination between these
weights.

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