WAVELET BASED COLOR HISTOGRAM IMAGE RETRIEVALijsetr.org/wp-content/uploads/2014/05/IJSETR-VOL-3-ISSUE-5-1524-1532.pdf · Index Terms—Image Retrieval, Color Histogram, Color Spaces,

International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 5, May 2014

ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR 1524

Abstract— In various application domains such as

education, crime prevention, commerce, and biomedicine, the

volume of digital data is increasing rapidly. The problem

appears when retrieving the information from the storage media.

Content-based image retrieval systems aim to retrieve images

from large image databases similar to the query image based on

the similarity between image features. We present a CBIR system

that uses the color feature as a visual feature to represent the

images. We use the images from the WANG database that is

widely used for CBIR performance evaluation. The database

contains color images, so we use the RGB color space to

represent the images. So we make use of the content based

image retrieval, using features like texture and color, called

WBCHIR (Wavelet Based Color Histogram Image

Retrieval).The texture and color features are extracted through

wavelet transformation and color histogram and the

combination of these features is robust to scaling and translation

of objects in an image. The proposed system has demonstrated a

promising and faster retrieval method on a WANG image

database containing 1000 general-purpose color images.

Index Terms—Image Retrieval, Color Histogram, Color Spaces,

Quantization, Similarity Matching, Haar Wavelet,

Precision and Recall

I. INTRODUCTION

With the development of the Internet, and the availability of

image capturing devices such as digital cameras, image

scanners, the size of digital image collection is increasing

rapidly. Efficient image searching, browsing and retrieval

tools are required by users from various domains, including

remote sensing, fashion, crime prevention, publishing, medicine, architecture, etc. For this purpose, many general

purpose image retrieval systems have been developed.

There are two frameworks: text-based and content-based.

The text-based approach can be traced back to 1970s. In

such systems, the images are manually annotated by text

descriptors, which are then used by a database management

system. (DBMS) to perform image retrieval.manual

annotation. The second is the inaccuracy in annotation due

to the subjectivity of human perception. To overcome these

disadvantages in text-based retrieval system, content-based

image retrieval (CBIR) was introduced in the early 1980s. In CBIR, images are indexed by their visual content, such

as color, texture, shapes. The fundamental difference

`

between content-based and text-based retrieval systems is

that the human interaction is an essential part of the latter

system. As a result, content-based image retrieval (CBIR)

from unannotated image databases has been a fast growing

research area recently.

The term Content-based image retrieval was originated in

1992, when it was used by T. Kato to describe experiments

into automatic retrieval of images from a database, based on

the colors and texture present. Since then, this term has

been used to describe the process of retrieving desired

images from a large collection on the basis of syntactical

image features. The techniques, tools and algorithms that

are used originate from fields such as statistics, pattern

recognition, signal processing, and computer vision.

1.1 Architecture Of CBIR

Content-based Image Retrieval (CBIR) is the searching of

an image database based on what is captured by the

individual images of the collection. There are various ways of implementing these searches and they will be explored

shortly. Content-based image retrieval (CBIR), the image

databases are indexed with descriptors derived from the

visual content of the images [2]. Most of the CBIR systems

are concerned with approximate queries where the aim is to

find images visually similar to a specified target image.

In most cases the aim of CBIR systems is to replicate

human perception of image similarity as well as possible.

The process of CBIR consists of the following stages:

(1) Image acquisition: to acquire a digital image.

Image Database: It consists of the collection of n

number of images depends on the user range and

choice.

(2) Image preprocessing: To improve the image in ways that increases the chances for success of the other

processes. The image is first processed in order to extract

the features, which describe its contents. The processing

involves filtering, normalization, segmentation, and object

identification. Like, image segmentation is the process of

dividing an image into multiple parts. The output of this

stage is a set of significant regions and objects.

WAVELET BASED COLOR HISTOGRAM

IMAGE RETRIEVAL

UdayaTheja.V, Sangamesh, Dr.Rajshekhar Ghogge



(3) Feature Extraction: Features such as shape, texture,

color, etc. are used to describe the content of the image. The

features further can be classified as low-level and high-level

features. In this step visual information is extracts from the

image and saves them as features vectors in a features

database .For each pixel, the image description is found in the form of feature value (or a set of value called a feature

vector) by using the feature extraction .These feature

vectors are used to compare the query with the other images

and retrieval.

(4) Similarity Matching: The information about each image

is stored in its feature vectors for computation process and

these feature vectors are matched with the feature vectors of

query image (the image to be search in the image database

whether the same image is present or not or how many are

similar kind images are exist or not) which helps in

measuring the similarity. This step involves the matching of the above stated features to yield a result that is visually

similar with the use of similarity measure method called as

Distance method. Here is different distances method

available such as Euclidean distance, City Block Distance,

Canberra Distance.

(5) Resultant Retrieved images: It searches the previously

maintained information to find the matched images from

database. The output will be the similar images having

same or very closest features as that of the query image.

(6) User interface and feedback which governs the display

of the outcomes, their ranking, the type of user interaction

with possibility of refining the search through some

automatic or manual preferences scheme etc.

Fig -1.1: CBIR System and its various components

A typical CBIR uses the contents of an image to represent

and access. CBIR systems extract features (color, texture, and shape) from images in the database based on the value

of the image pixels. These features are smaller than the

image size and stored in a database called feature database.

Thus the feature database contains an abstraction (compact

form) of the images in the image database; each image is

represented by a compact representation of its contents

(color, texture, shape and spatial information) in the form of

a fixed length real-valued multi-component feature vectors

or signature. This is called off-line feature extraction.

When the user submits a query image to the CBIR system,

the system automatically extracts the features of the query

image in the same way as it does for the image database.

The distance (similarity) between the feature vector of the

query image and the feature vectors stored in the feature

database are computed. The system will sort and retrieve

the best similar images according to their similarity values.

This is called on-line image retrieval. The main advantage of using CBIR system is that the system uses image features

instead of using the image itself. So, CBIR is cheap, fast

and efficient over image search methods.

We propose an image retrieval system, called Wavelet-Based Color Histogram Image Retrieval (WBCHIR), based

on the combination of color and texture features. The color

histogram for color feature and wavelet representation for

texture and location information of an image. This reduces

the processing time for retrieval of an image with more

promising representatives.

1.2 The Importance Of Content Based Image Retrieval

To search for an image, we use some text or a keyword that

describes the image to retrieve it. This method is not good

for image retrieving, because in that case, every image must

have a powerful complete description and then must match

the words we use to search. Unfortunately, we have huge

image databases, and it is illogical to describe every image

in the database with a good complete description and when we retrieve the images, the system will often miss some

images and will retrieve images that don't relate to what we

need. From this point, we want to find a new technique and

use it to retrieve images depending on its content not its

description.

To solve the problem of searching for an image using text,

we will use the content of the image to search and retrieve

it. CBIR is a technique to search and retrieve images. A

content-based retrieval system processes the information

contained in image data and creates an abstraction of its

content in terms of visual attributes. These attributes are

color, shape, and texture. Any query operations deal with

this abstraction rather than with the image itself. Thus,

every image inserted into the database is analyzed, and a

compact representation of its content is stored in a feature

vector, or signature.

To retrieve an image, the query image must compare with

other images in the database for similarity. Similarity

comparison uses the image representation. Representation of an image includes extracting some features. Features

extracted from an image can be color, texture, or shape. The

similarity is to calculate the difference between the images'

features. For this point, CBIR has several advantages

comparing with other approaches such we have mentioned

such as text-based retrieval.



1.3 Applications

CBIR concepts have been used widely in many real

applications. Most of our fields need image processing and

retrieving such as medical, architectural, criminal, and in

the web. For the medical field, CBIR is used for diagnosis by identifying similar past cases. In critical buildings, this

technique is used for finger print or retina scanning for

privileges. The most important application that uses CBIR

is the web. Many web applications provide searching and

retrieving images based on their contents. In general,

retrieving images based on their content becomes serious

and important techniques in most of the human

applications. Potentially fruitful areas include:

Crime Prevention

Nowadays, police forces keep large archives of evidence for past suspects, including facial photographs and

fingerprints. When a crime is happened, they take the

evidence from the scene of the crime and compare it with

the records in their archives. They use CBIR systems to get

their results. The most import thing when designing these

systems is the ability to search an entire database to find the

closest matching records instead of matching against only a

single stored record.

Medical Diagnosis

Modern medicine depends on diagnostic methods

such as radiology, histopathology, and computerized

tomography. These diagnostic methods have resulted in a

large number of important medical images that most

hospitals stored. Now, there is a great interest to use of

CBIR methods to aid diagnosis by identifying similar past

cases.

Home Entertainment

Most home entertainment is images such as

holiday images, festivals and videos such as favorite programs and movies. CBIR methods can be used for image

management. Now, number of large organizations devotes

large development effort to design simple software for

retrieval with affordable price.

Web Searching

One of the most CBIR applications is the web

searching. Uses face problems when they search for certain

images by some image description. Many search engines

use the text-based search and for many times the results are not stratified by the user. Some content-based search

engines are developed so that the user submits the query

image and the search engine retrieve the most similar

images. Some content-based search engines provide a

relevance feedback facility to refine search results.

Numerous commercial and experimental CBIR systems are

now available, and many web search engines are now

equipped with CBIR facilities, as for example Alta Vista,

Yahoo and Google.

II. FUNDAMENTALS OF IMAGE RETRIEVAL

The main idea behind CBIR systems is to allow users to

find images that are visually similar to the query image.

Similar may have different meanings. Some users may be

interested in some image regions. Others are interested in some shapes and the color of them. Therefore, different

needs mean different methods for similarity. To allow

different methods for similarity, different image descriptors

are needed. Image descriptors may account for different

properties of images. Image descriptors mean image

features. A feature means anything that is localized,

meaningful and detectable. If we talk about image features,

we mean objects in that image such as corners, lines,

shapes, textures, and motions. Features extracted from an

image describe and define the content of that image.

Intuitively, the most direct method to compare two images

is to compare the pixels in one image to the corresponding

pixels in the other image. Clearly, this method is not

feasible, because images may have different size that

applications cannot determine which pixels from one image correspond to which pixels in the other image. Another

reason is the computational complexity [4]. When a system

wants to match two images by comparing pixel by pixel, it

will take a long time. This is just for two images.

Nowadays, we talk about thousands of images stored in

databases that are used for image retrieving. Comparing

images using their pixels is time consuming. More powerful

method is to use image features instead of using the original

pixel values because of the significant simplification of

image representation, and the easy way to compare images

using their features.

A wide variety of features had been considered for image

retrieval. Color, texture, and shape are some image features

that can be used to describe an image. However, no

particular feature is most suitable for retrieving all types of

images [5]. Color images need color features that are most suitable to describe them. Images containing visual

patterns, surface properties, and scene need texture features

to describe them. In reality, no one particular feature can

describe an image completely. Many images have to be

described by more than one feature. For example, color and

texture features are best features to describe natural scenes.

Features extracted from the image are used for computing

the similarity between images. Some measurement methods

are used to calculate the similarity between images. In this

chapter, we will define image features, explaining their

properties. We introduce some methods for similarity

measures.

2.1 Feature Extraction

Feature extraction means obtaining useful information that

can describe the image with its content. We mean by image

features the characteristic properties. For example, the

image of a forest can be described by its green color and



some texture of trees. Objects in the image can be

considered as shapes that can be a feature for the image. To

describe an image, we have to consider its main features.

Selecting image features is an important step so that it can

represent the content of the image very well. Color and

texture are some features considered for content image

description.

2.2 Color

Color is the sensation caused by the light as it interacts with our eyes and brain. Color features are the fundamental

characteristics of the content of images. Human eyes are

sensitive to colors, and color features enable human to

distinguish between objects in the images. Colors are used

in image processing because they provide powerful

descriptors that can be used to identify and extract objects

from a scene. Color features provide sometimes powerful

information about images, and they are very useful for

image retrieval.

To facilitate the specification of colors in some standard,

color spaces (also called color models or color systems) are

proposed. A color space is a specification of a coordinate

system and a subspace within the system where each color

is represented by a single point. Today, most color spaces in

use are oriented toward hardware (such as for color monitors and printers) or toward software for applications

where color manipulation is the target. In most digital

image processing, RGB (red, green, blue) color space is

used in practice for color monitors and CMY (cyan,

magenta, yellow) color space is used for color printing. In

our work, we are focusing on the RGB color space.

To extract the color features from the content of an image,

we need to select a color space and use its properties in the

extraction. In common, colors are defined in three

dimensional color space. The purpose of the color space is

to facilitate the specification of colors in some standard,

accepted way. Several color spaces are used to represent

images for different purposes. The RGB color space is the

most widely used color space. RGB stands for Red, Green,

and Blue. RGB color space combines the three colors in different ratio to create other colors. In digital image

purposes, RGB color space is the most prevalent choice.

The main drawback of the RGB color space is that it is

perceptually non uniform. We can imagine the RGB color

space as a unit cube with red, green, and blue axes. Any

color in the RGB color space can be represented by a vector

of three coordinates. To overcome the drawback of the

RGB color space, different color spaces are proposed.

The HSx color space is commonly used in digital image

processing that converts the color space of the image from

RGB color space to one of the HSx color spaces. HSx color

space contains the HSI, HSV, HSB color spaces. They are

common to human color perception. HS stands for Hue and

Saturation. I, V, and B stand for Intensity, Value, and

Brightness, respectively. The different difference between

them is their transformation method from the RGB color

space. Hue describes the actual wavelength of the color.

Saturation is the measure of the purity of the color. For

example, red is 100% saturated color, but pink is not 100%

saturated color because it contains an amount of white.

Intensity describes the lightness of the color. HSV color space is the most widely used when converting the color

space from RGB color space.

2.2.1 Methods of Representation

The main method of representing color information of

images in CBIR systems is through color histograms. A

color histogram is a type of bar graph, where each bar

represents a particular color of the color space being used.

In Mat Lab for example you can get a color histogram of an

image in the RGB or HSV color space. The bars in a color

histogram are referred to as bins and they represent the x-

axis. The number of bins depends on the number of colors

there are in an image. The y-axis denotes the number of

pixels there are in each bin. In other words how many

pixels in an image are of a particular color.

An example of a color histogram in the HSV color space

can be seen with the following image:

Fig -2.1: Sample Image and its Corresponding HistogramTo view a

histogram numerically one has to look at the color map or the numeric

representation of each bin.



Table -2.1: Color Map and Number of pixels for the Previous Image.

As one can see from the color map each row represents the

color of a bin. The row is composed of the three coordinates

of the color space. The first coordinate represents hue, the

second saturation, and the third, value, thereby giving HSV.

The percentages of each of these coordinates are what make

up the color of a bin. Also one can see the corresponding

pixel numbers for each bin, which are denoted by the blue

lines in the histogram.

Quantization in terms of color histograms refers to the

process of reducing the number of bins by taking colors that

are very similar to each other and putting them in the same

bin. By default the maximum number of bins one can obtain using the histogram function in MatLab is 256. For

the purpose of saving time when trying to compare color

histograms, one can quantize the number of bins [3].

Obviously quantization reduces the information regarding

the content of images but as was mentioned this is the

tradeoff when one wants to reduce processing time.

There are two types of color histograms, Global color

histograms (GCHs) and Local color histograms (LCHs). A

GCH represents one whole image with a single color

histogram. An LCH divides an image into fixed blocks and

takes the color histogram of each of those blocks. LCHs

contain more information about an image but are

computationally expensive when comparing images. “The

GCH is the traditional method for color based image

retrieval. However, it does not include information concerning the color distribution of the regions of an image.

Thus when comparing GCHs one might not always get a

proper result in terms of similarity of images.

2.3 Texture

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 physical composition of a surface.

Texture properties include: Coarseness, Contrast,

Directionality, Line-likeness, Regularity, and Roughness.

Texture is one of the most important defining features of an image. It is characterized by the spatial distribution of gray

levels in a neighborhood. In order to capture the spatial

dependence of gray-level values, which contribute to the

perception of texture, a two-dimensional dependence

texture analysis matrix is taken into consideration. This

two-dimensional matrix is obtained by decoding the image

file; jpeg, bmp, etc.

2.3.1 Methods of Representation

There are three principal approaches used to

describe texture; statistical, structural and spectral

Statistical techniques characterize textures using

the statistical properties of the grey levels of the

points/pixels comprising a surface image.

Typically, these properties are computed using: the

grey level co-occurrence matrix of the surface, or

the wavelet transformation of the surface.

Structural techniques characterize textures as being

composed of simple primitive structures called “texels” (or texture elements). These are arranged

regularly on a surface according to some surface

arrangement rules.

Fig -2.2: Examples of Textures

Clouds

Bricks

Rocks



Spectral techniques are based on properties of the

Fourier spectrum and describe global periodicity

of the grey levels of a surface by identifying high-

energy peaks in the Fourier spectrum. For

optimum classification purposes, what concern us

are the statistical techniques of characterization. This is because it is these techniques that result in

computing texture properties. The most popular

statistical representations of texture are: Co-

occurrence Matrix, Tamura Texture, and Wavelet

Transform.

III. IMAGE RETRIEVAL BASED ON CONTENT

We introduce our proposed CBIR system. In our proposed

system, we will extract some color features to represent the

image and use these features to compare between the

images.

3.1 Color Feature Extraction

The extraction of color features from digital images

depends on an understanding of the theory of color and the representation of color in digital images. The color

histogram is one of the most commonly used color feature

representation in image retrieval. The power to identify an

object using color is much larger than that of a gray scale.

3.1.1 Color Space Selection And Color Quantization

The color of an image is represented, through any of the

popular color spaces like RGB, XYZ, YIQ, L*a*b*,

U*V*W*, YUV and HSV. It has been reported that the

HSV color space gives the best color histogram feature,

among the different color spaces. In HSV color space the

color is presented in terms of three components: Hue (H),

Saturation (S) and Value (V) and the HSV color space is

based on cylinder coordinates.

Color quantization is a process that optimizes the use of

distinct colors in an image without affecting the visual

properties of an image. For a true color image, the distinct

number of colors is up to 224 = 16777216 and the direct

extraction of color feature from the true color will lead to a large computation. In order to reduce the computation, the

color quantization can be used to represent the image,

without a significant reduction in image quality, thereby

reducing the storage space and enhancing the process

speed.

3.1.2 Color Histogram

A color histogram represents the distribution of

colors in an image, through a set of bins, where each

histogram bin corresponds to a color in the quantized color

space. A color histogram for a given image is represented

by a vector:

H = {H[0] , H[1] , H[2] , H[3] ,

H[4],……….H[i],…….,H[n]}.

Where i is the color bin in the color histogram and H[i]

represents the number of pixels of color i in the image, and

n is the total number of bins used in color histogram.

Typically, each pixel in an image will be assigned to a bin

of a color histogram. Accordingly in the color histogram of an image, the value of each bin gives the number of pixels

that has the same corresponding color. In order to compare

images of different sizes, color histograms should be

normalized. The normalized color histogram H` is given as:

Where p is the total number of pixels of an image.

3.2 Texture Feature Extraction

Like color, the texture is a powerful low-level feature for

image search and retrieval applications. Much work has

been done on texture analysis, classification, and

segmentation for the last four decade, still there is a lot of

potential for the research. So far, there is no unique

definition for texture; however, an encapsulating scientific

definition as given in can be stated as, “Texture is an attribute representing the spatial arrangement of the grey

levels of the pixels in a region or image”. The common

known texture descriptors are Wavelet Transform, Gabor-

filter, co-occurrence matrices and Tamura features. We

have used Wavelet Transform, which decomposes an image

into orthogonal components, because of its better

localization and computationally inexpensive properties.

3.2.1 Haar Discrete Wavelet Transforms

Discrete wavelet transformation (DWT) is used to

transform an image from spatial domain into frequency

domain. The wavelet transform represents a function as a

superposition of a family of basis functions called wavelets.

Wavelet transforms extract information from signal at

different scales by passing the signal through low pass and high pass filters. Wavelets provide multi-resolution

capability and good energy compaction. Wavelets are

robust with respect to color intensity shifts and can capture

both texture and shape information efficiently. The wavelet

transforms can be computed linearly with time and thus

allowing for very fast algorithms. DWT decomposes a

signal into a set of Basis Functions and Wavelet Functions.

The wavelet transform computation of a two-dimensional

image is also a multi-resolution approach, which applies

recursive filtering and sub-sampling. At each level (scale),

the image is decomposed into four frequency sub-bands, LL, LH, HL, and HH where L denotes low frequency and H

denotes high frequency as shown in Figure.



Fig -3.1: Discrete Wavelet Sub-band

Decomposition

Haar wavelets are widely being used since its invention

after by Haar. Haar used these functions to give an example

of a countable orthonormal system for the space of square-

integrable functions on the real line. Here, we have used

Haar wavelets to compute feature signatures, because they

are the fastest to compute and also have been found to

perform well in practice. Haar wavelets enable us to speed

up the wavelet computation phase for thousands of sliding

windows of varying sizes in an image. The Haar wavelet's

mother wavelet function can be described as:

And its scaling function can be described as:

3.3 Feature Similarity Matching

The Similarity matching is the process of approximating a

solution, based on the computation of a similarity function

between a pair of images, and the result is a set of likely

values. Exactness, however, is a precise concept.

3.3.1 Histogram Intersection Distance

Swain and Ballard [4] proposed histogram intersection for

color image retrieval. Intersection of histograms was

originally defined as:

Smith and Chang [6] extended the idea, by modifying the

denominator of the original definition, to include the case

when the cardinalities of the two histograms are different

and expressed as:

and |Q| and |D| represents the magnitude of the histogram

for query image and a representative image in the Database.

IV. PROPOSED METHODOLOGY

Proposing two algorithms for image retrieval based on the

color histogram and Wavelet-based Color Histogram.

4.1 Color Histogram

Input: Query Image.

Output: Retrieved Images

Method: 1. Convert RGB color space image into HSV color space.

2. Color quantization is carried out using color histogram

by assigning 8 level each to

hue, saturation and value to give a quantized HSV space

with 8x8x8=512 histogram

bins.

3. The normalized histogram is obtained by dividing with

the total number of pixels.

4. Repeat step1 to step3 on an image in the database.

5. Calculate the similarity matrix of query image and the

image present in the database. 6. Repeat the steps from 4 to 5 for all the images in the

database.

7. Retrieve the images.

Fig -4.1: Block diagram of proposed Color Histogram

4.2 Wavelet-Based Color Histogram (Wbch)

Input: Query Image.

Output: Retrieved Images.

Method:

1. Extract the Red, Green, and Blue Components from an

image.



2. Decompose each Red, Green, Blue Component using

Haar Wavelet transformation at

1st level to get approximate coefficient and vertical,

horizontal and diagonal detail

Coefficients.

3. Combine approximate coefficient of Red, Green, and Blue Component.

4. Similarly combine the horizontal and vertical coefficients

of Red, Green, and Blue

Component.

5. Assign the weights 0.003 to approximate coefficients,

0.001 to horizontal and 0.001 to

Vertical coefficients (experimentally observed values).

6. Convert the approximate, horizontal and vertical

coefficients into HSV plane.

7. Color quantization is carried out using color histogram

by assigning 8 level each to hue,

Saturation and value to give a quantized HSV space with 8x8x8=512 histogram bins.

8. The normalized histogram is obtained by dividing with

the total number of pixels.

9. Repeat step1 to step8 on an image in the database.

10. Calculate the similarity matrix of query image and the

image present in the database.

11. Repeat the steps from 9 to 10 for all the images in the

database.

12. Retrieve the images.

Fig -4.2: Block diagram of proposed Wavelet-Based Color

Histogram (WBCH). (A-approximate

coefficient, H-horizontal detail coefficient, V-vertical detail

coefficient).

4.3 Performance Evaluation

The performance of retrieval of the system can be measured

in terms of its recall and precision. Recall measures the

ability of the system to retrieve all the models that are

relevant, while precision measures the ability of the system

to retrieve only the models that are relevant. It has been

reported that the histogram gives the best performance

through recall and precision value They are defined as:

Where A represent the number of relevant images that are

retrieved, B, the number of irrelevant items and the C,

number of relevant items those were not retrieved. The number of relevant items retrieved is the number of the

returned images that are similar to the query image in this

case. The total number of items retrieved is the number of

images that are returned by the search engine.

The average precision for the images that belongs to the qth

category (Aq) has been computed by

4.4 Example

Fig -4.3: Retrieval Results of Bus



Fig -4.4: Retrieval Results of Elephant

V. CONCLUSION

We presented a novel approach for Content Based Image

Retrieval by combining the color and texture features called

Wavelet-Based Color Histogram Image Retrieval

(WBCHIR). Similarity between the images is ascertained

by means of a distance function. The proposed method

outperforms the other retrieval methods in terms of Average

Precision. Moreover, the computational steps are effectively

reduced with the use of Wavelet transformation. As a result, there is a substational increase in the retrieval speed. The

whole indexing time for the 1000 image database takes 5-6

minutes.

One limitation in our work is that the color feature is not enough to represent the image and use it for similarity

matching. There are some retrieved images which are not

similar to the query .The proposed system matches the

images if the dominant color is similar. We can overcome

this limitation by using more than one feature to represent

the image.

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[2] R. Chakarvarti and X. Meng, “A Study of Color Histogram Based

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UdayaTheja.V, M.Tech(CNE), Dr.AIT, Bangalore, India1

Sangamesh, M.Tech( CNE), Dr.AIT, Bangalore, India 2

Dr.Rajshekhar Ghogge, Assistant Professor, Dept of ISE,

Dr.AIT, Bangalore, India3

WAVELET BASED COLOR HISTOGRAM IMAGE RETRIEVALijsetr.org/wp-content/uploads/2014/05/IJSETR-VOL-3-ISSUE-5-1524-1532.pdf · Index Terms—Image Retrieval, Color Histogram, Color Spaces,

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