This document describes a Matlab software application for similar image retrieval that uses shape and color features. It discusses extracting edge features through horizontal, vertical, diagonal and anti-diagonal masks. It also discusses extracting color features by converting images to HSV color space and generating 128-bin color histograms. Features of images in a database are stored in a 'features.mat' file. For a query image, its features are compared to those in the database using Euclidean distance to find similar images. The most similar images based on feature similarity are displayed at the top of the results. The software provides image retrieval based on fusing color and shape features.
A comparative analysis of retrieval techniques in content based image retrievalcsandit
Basic group of visual techniques such as color, shape, texture are used in Content Based Image
Retrievals (CBIR) to retrieve query image or sub region of image to find similar images in
image database. To improve query result, relevance feedback is used many times in CBIR to
help user to express their preference and improve query results. In this paper, a new approach
for image retrieval is proposed which is based on the features such as Color Histogram, Eigen
Values and Match Point. Images from various types of database are first identified by using
edge detection techniques .Once the image is identified, then the image is searched in the
particular database, then all related images are displayed. This will save the retrieval time.
Further to retrieve the precise query image, any of the three techniques are used and
comparison is done w.r.t. average retrieval time. Eigen value technique found to be the best as
compared with other two techniques.
Research Inventy : International Journal of Engineering and Scienceinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
A comparative analysis of retrieval techniques in content based image retrievalcsandit
Basic group of visual techniques such as color, shape, texture are used in Content Based Image
Retrievals (CBIR) to retrieve query image or sub region of image to find similar images in
image database. To improve query result, relevance feedback is used many times in CBIR to
help user to express their preference and improve query results. In this paper, a new approach
for image retrieval is proposed which is based on the features such as Color Histogram, Eigen
Values and Match Point. Images from various types of database are first identified by using
edge detection techniques .Once the image is identified, then the image is searched in the
particular database, then all related images are displayed. This will save the retrieval time.
Further to retrieve the precise query image, any of the three techniques are used and
comparison is done w.r.t. average retrieval time. Eigen value technique found to be the best as
compared with other two techniques.
Research Inventy : International Journal of Engineering and Scienceinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Image fusion is a technique used to integrate a highresolution
panchromatic image with multispectral low-resolution
image to produce a multispectral high-resolution image, that
contains both the spatial information of the panchromatic highresolution
image and the color information of the multispectral
image .Although an increasing number of high-resolution images
are available along with sensor technology development, the
process of image fusion is still a popular and important method to
interpret the image data for obtaining a more suitable image for a
variety of applications, like visual interpretation and digital
classification. To get the complete information from the single
image we need to have a method to fuse the images. In the current
paper we are going to propose a method that uses hybrid of
wavelets for Image fusion.
Textural Feature Extraction of Natural Objects for Image ClassificationCSCJournals
The field of digital image processing has been growing in scope in the recent years. A digital image is represented as a two-dimensional array of pixels, where each pixel has the intensity and location information. Analysis of digital images involves extraction of meaningful information from them, based on certain requirements. Digital Image Analysis requires the extraction of features, transforms the data in the high-dimensional space to a space of fewer dimensions. Feature vectors are n-dimensional vectors of numerical features used to represent an object. We have used Haralick features to classify various images using different classification algorithms like Support Vector Machines (SVM), Logistic Classifier, Random Forests Multi Layer Perception and Naïve Bayes Classifier. Then we used cross validation to assess how well a classifier works for a generalized data set, as compared to the classifications obtained during training.
Digital Image Processing (Lab 1)
Course Objectives: To learn the fundamental concepts of Digital Image Processing and to study basic image processing operations.
Comparision of Clustering Algorithms usingNeural Network Classifier for Satel...IJERA Editor
This paper presents a hybrid clustering algorithm and feed-forward neural network classifier for land-cover mapping of trees, shade, building and road. It starts with the single step preprocessing procedure to make the image suitable for segmentation. The pre-processed image is segmented using the hybrid genetic-Artificial Bee Colony(ABC) algorithm that is developed by hybridizing the ABC and FCM to obtain the effective segmentation in satellite image and classified using neural network . The performance of the proposed hybrid algorithm is compared with the algorithms like, k-means, Fuzzy C means(FCM), Moving K-means, Artificial Bee Colony(ABC) algorithm, ABC-GA algorithm, Moving KFCM and KFCM algorithm.
A Biometric Approach to Encrypt a File with the Help of Session KeySougata Das
The main objective of this work is to provide a two layer authentication system through biometric (face) and conventional session based password authentication. The encryption key for this authentication will be generated with the combination of the biometric key and session based password.
Quality Assessment of Gray and Color Images through Image Fusion TechniqueIJEEE
. Image fusion is an emerging trend in the digital image processing to enhance images. In image fusion two or more images can be fused (combined) to obtain an enhanced image. In the present work image fusion technology has been used to enhance a given input image. Image fusion is used here to combine two images which contains complementary information.
The students can learn about basics of image processing using matlab.
It explains the image operations with the help of examples and Matlab codes.
Students can fine sample images and .m code from the link given in slides.
Template matching is a technique in computer vision used for finding a sub-image of a target image which matches a template image. This technique is widely used in object detection fields such as vehicle tracking, robotics , medical imaging, and manufacturing .
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Novel Super Resolution Algorithm Using Interpolation and LWT Based Denoisin...CSCJournals
Image capturing technique has some limitations and due to that we often get low resolution(LR) images. Super Resolution(SR) is a process by which we can generate High Resolution(HR) image from one or more LR images. Here we have proposed one SR algorithm which take three shifted and noisy LR images and generate HR image using Lifting Wavelet Transform(LWT) based denoising method and Directional Filtering and Data Fusion based Edge-Guided Interpolation Algorithm.
An Improved Way of Segmentation and Classification of Remote Sensing Images U...ijsrd.com
The Ultimate significance of Images lies in processing the digital image which stems from two principal application areas: Advances of pictorial information for human interpretation; and dispensation of image data for storage, communication, and illustration for self-sufficient machine perception. The objective of this research work is to define the meaning and possibility of image segmentation based on remote sensing images which are successively classified with statistical measures. In this paper kernel induced Possiblistic C-means clustering algorithm has been implemented for classifying remote sensing image data with image features. As a final point of the proposed work is to point out that this algorithm works well for segmenting and classifying the image with better accuracy with statistical metrices.
A comparative study on content based image retrieval methodsIJLT EMAS
Content-based image retrieval (CBIR) is a method of
finding images from a huge image database according to persons’
interests. Content-based here means that the search involves
analysis the actual content present in the image. As database of
images is growing daybyday, researchers/scholars are searching
for better techniques for retrieval of images maintaining good
efficiency. This paper presents the visual features and various
ways for image retrieval from the huge image database.
Image fusion is a technique used to integrate a highresolution
panchromatic image with multispectral low-resolution
image to produce a multispectral high-resolution image, that
contains both the spatial information of the panchromatic highresolution
image and the color information of the multispectral
image .Although an increasing number of high-resolution images
are available along with sensor technology development, the
process of image fusion is still a popular and important method to
interpret the image data for obtaining a more suitable image for a
variety of applications, like visual interpretation and digital
classification. To get the complete information from the single
image we need to have a method to fuse the images. In the current
paper we are going to propose a method that uses hybrid of
wavelets for Image fusion.
Textural Feature Extraction of Natural Objects for Image ClassificationCSCJournals
The field of digital image processing has been growing in scope in the recent years. A digital image is represented as a two-dimensional array of pixels, where each pixel has the intensity and location information. Analysis of digital images involves extraction of meaningful information from them, based on certain requirements. Digital Image Analysis requires the extraction of features, transforms the data in the high-dimensional space to a space of fewer dimensions. Feature vectors are n-dimensional vectors of numerical features used to represent an object. We have used Haralick features to classify various images using different classification algorithms like Support Vector Machines (SVM), Logistic Classifier, Random Forests Multi Layer Perception and Naïve Bayes Classifier. Then we used cross validation to assess how well a classifier works for a generalized data set, as compared to the classifications obtained during training.
Digital Image Processing (Lab 1)
Course Objectives: To learn the fundamental concepts of Digital Image Processing and to study basic image processing operations.
Comparision of Clustering Algorithms usingNeural Network Classifier for Satel...IJERA Editor
This paper presents a hybrid clustering algorithm and feed-forward neural network classifier for land-cover mapping of trees, shade, building and road. It starts with the single step preprocessing procedure to make the image suitable for segmentation. The pre-processed image is segmented using the hybrid genetic-Artificial Bee Colony(ABC) algorithm that is developed by hybridizing the ABC and FCM to obtain the effective segmentation in satellite image and classified using neural network . The performance of the proposed hybrid algorithm is compared with the algorithms like, k-means, Fuzzy C means(FCM), Moving K-means, Artificial Bee Colony(ABC) algorithm, ABC-GA algorithm, Moving KFCM and KFCM algorithm.
A Biometric Approach to Encrypt a File with the Help of Session KeySougata Das
The main objective of this work is to provide a two layer authentication system through biometric (face) and conventional session based password authentication. The encryption key for this authentication will be generated with the combination of the biometric key and session based password.
Quality Assessment of Gray and Color Images through Image Fusion TechniqueIJEEE
. Image fusion is an emerging trend in the digital image processing to enhance images. In image fusion two or more images can be fused (combined) to obtain an enhanced image. In the present work image fusion technology has been used to enhance a given input image. Image fusion is used here to combine two images which contains complementary information.
The students can learn about basics of image processing using matlab.
It explains the image operations with the help of examples and Matlab codes.
Students can fine sample images and .m code from the link given in slides.
Template matching is a technique in computer vision used for finding a sub-image of a target image which matches a template image. This technique is widely used in object detection fields such as vehicle tracking, robotics , medical imaging, and manufacturing .
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Novel Super Resolution Algorithm Using Interpolation and LWT Based Denoisin...CSCJournals
Image capturing technique has some limitations and due to that we often get low resolution(LR) images. Super Resolution(SR) is a process by which we can generate High Resolution(HR) image from one or more LR images. Here we have proposed one SR algorithm which take three shifted and noisy LR images and generate HR image using Lifting Wavelet Transform(LWT) based denoising method and Directional Filtering and Data Fusion based Edge-Guided Interpolation Algorithm.
An Improved Way of Segmentation and Classification of Remote Sensing Images U...ijsrd.com
The Ultimate significance of Images lies in processing the digital image which stems from two principal application areas: Advances of pictorial information for human interpretation; and dispensation of image data for storage, communication, and illustration for self-sufficient machine perception. The objective of this research work is to define the meaning and possibility of image segmentation based on remote sensing images which are successively classified with statistical measures. In this paper kernel induced Possiblistic C-means clustering algorithm has been implemented for classifying remote sensing image data with image features. As a final point of the proposed work is to point out that this algorithm works well for segmenting and classifying the image with better accuracy with statistical metrices.
A comparative study on content based image retrieval methodsIJLT EMAS
Content-based image retrieval (CBIR) is a method of
finding images from a huge image database according to persons’
interests. Content-based here means that the search involves
analysis the actual content present in the image. As database of
images is growing daybyday, researchers/scholars are searching
for better techniques for retrieval of images maintaining good
efficiency. This paper presents the visual features and various
ways for image retrieval from the huge image database.
A COMPARATIVE ANALYSIS OF RETRIEVAL TECHNIQUES IN CONTENT BASED IMAGE RETRIEVALcscpconf
Basic group of visual techniques such as color, shape, texture are used in Content Based Image Retrievals (CBIR) to retrieve query image or sub region of image to find similar images in image database. To improve query result, relevance feedback is used many times in CBIR to help user to express their preference and improve query results. In this paper, a new approach for image retrieval is proposed which is based on the features such as Color Histogram, Eigen Values and Match Point. Images from various types of database are first identified by using edge detection techniques .Once the image is identified, then the image is searched in the particular database, then all related images are displayed. This will save the retrieval time. Further to retrieve the precise query image, any of the three techniques are used and comparison is done w.r.t. average retrieval time. Eigen value technique found to be the best as compared with other two techniques.
Query Image Searching With Integrated Textual and Visual Relevance Feedback f...IJERA Editor
There are many researchers who have studied the relevance feedback in the literature of content based image
retrieval (CBIR) community, but none of CBIR search engines support it because of scalability, effectiveness
and efficiency issues. In this, we had implemented an integrated relevance feedback for retrieving of web
images. Here, we had concentrated on integration of both textual features (TF) and visual features (VF) based
relevance feedback (RF), simultaneously we also tested them individually. The TFRF employs and effective
search result clustering (SRC) algorithm to get salient phrases. Then a new user interface (UI) is proposed to
support RF. Experimental results show that the proposed algorithm is scalable, effective and accurated
SEMANTIC IMAGE RETRIEVAL USING MULTIPLE FEATUREScscpconf
In Content Based Image Retrieval (CBIR) some problem such as recognizing the similar
images, the need for databases, the semantic gap, and retrieving the desired images from huge
collections are the keys to improve. CBIR system analyzes the image content for indexing,
management, extraction and retrieval via low-level features such as color, texture and shape.
To achieve higher semantic performance, recent system seeks to combine the low-level features
of images with high-level features that conation perceptual information for human beings.
Performance improvements of indexing and retrieval play an important role for providing
advanced CBIR services. To overcome these above problems, a new query-by-image technique
using combination of multiple features is proposed. The proposed technique efficiently sifts through the dataset of images to retrieve semantically similar images.
Content Based Image Retrieval (CBIR) is one of the
most active in the current research field of multimedia retrieval.
It retrieves the images from the large databases based on images
feature like color, texture and shape. In this paper, Image
retrieval based on multi feature fusion is achieved by color and
texture features as well as the similarity measures are
investigated. The work of color feature extraction is obtained by
using Quadratic Distance and texture features by using Pyramid
Structure Wavelet Transforms and Gray level co-occurrence
matrix. We are comparing all these methods for best image
retrieval
EFFICIENT APPROACH FOR CONTENT BASED IMAGE RETRIEVAL USING MULTIPLE SVM IN YA...cscpconf
Due to the enormous increase in image database sizes, the need for an image search and
indexing tool is crucial. Content-based image retrieval systems (CBIR) have become very
popular for browsing, searching and retrieving images in different fields including web based
searching, industry inspection, satellite images, medical diagnosis images, etc. The challenge,
however, is in designing a system that returns a set of relevant images i.e. if the query image
represents a horse then the first images returned from a large image dataset must return horse
images as first responses. In this paper, we have combined YACBIR [7], a CBIR that relies on
color, texture and points of interest and Multiple Support Vector Machines Ensemble to reduce
the existing gap between high-level semantic and low-level descriptors and enhance the
performance of retrieval by minimize the empirical classification error and maximize the
geometric margin classifiers. The experimental results show that the method proposed reaches
high recall and precision.
Efficient Approach for Content Based Image Retrieval Using Multiple SVM in YA...csandit
Due to the enormous increase in image database sizes, the need for an image search and indexing tool is crucial. Content-based image retrieval systems (CBIR) have become very popular for browsing, searching and retrieving images in different fields including web based searching, industry inspection, satellite images, medical diagnosis images, etc. The challenge, however, is in designing a system that returns a set of relevant images i.e. if the query image represents a horse then the first images returned from a large image dataset must return horse images as first responses. In this paper, we have combined YACBIR [7], a CBIR that relies on color, texture and points of interest and Multiple Support Vector Machines Ensemble to reduce the existing gap between high-level semantic and low-level descriptors and enhance the performance of retrieval by minimize the empirical classification error and maximize the geometric margin classifiers. The experimental results show that the method proposed reaches high recall and precision.
Video surveillance is active research topic in
computer vision research area for humans & vehicles, so it is
used over a great extent. Multiple images generated using a fixed
camera contains various objects, which are taken under different
variations, illumination changes after that the object’s identity
and orientation are provided to the user. This scheme is used to
represent individual images as well as various objects classes in a
single, scale and rotation invariant model.The objective is to
improve object recognition accuracy for surveillance purposes &
to detect multiple objects with sufficient level of scale
invariance.Multiple objects detection& recognition is important
in the analysis of video data and higher level security system. This
method can efficiently detect the objects from query images as
well as videos by extracting frames one by one. When given a
query image at runtime, by generating the set of query features
and it will find best match it to other sets within the database.
Using SURF algorithm find the database object with the best
feature matching, then object is present in the query image.
An image is a medium for conveying information. The information contained therein may be a particular event, experience or moment. Not infrequently many images that have similarities. However, this level of similarity is not easily detected by the human eye. Eigenface is one technique to calculate the resemblance of an object. This technique calculates based on the intensity of the colors that exist in the two images compared. The stages used are normalization, eigenface, training, and testing. Eigenface is used to calculate pixel proximity between images. This calculation yields the feature value used for comparison. The smallest value of the feature value is an image very close to the original image. Application of this method is very helpful for analysts to predict the likeness of digital images. Also, it can be used in the field of steganography, digital forensic, face recognition and so forth.
Web Image Retrieval Using Visual Dictionaryijwscjournal
In this research, we have proposed semantic based image retrieval system to retrieve set of relevant images for the given query image from the Web. We have used global color space model and Dense SIFT feature extraction technique to generate visual dictionary using proposed quantization algorithm. The images are transformed into set of features. These features are used as inputs in our proposed Quantization algorithm for generating the code word to form visual dictionary. These codewords are used to represent images semantically to form visual labels using Bag-of-Features (BoF). The Histogram intersection method is used to measure the distance between input image and the set of images in the image database to retrieve similar images. The experimental results are evaluated over a collection of 1000 generic Web images to demonstrate the effectiveness of the proposed system.
Web Image Retrieval Using Visual Dictionaryijwscjournal
In this research, we have proposed semantic based image retrieval system to retrieve set of relevant images for the given query image from the Web. We have used global color space model and Dense SIFT feature extraction technique to generate visual dictionary using proposed quantization algorithm. The images are transformed into set of features. These features are used as inputs in our proposed Quantization algorithm for generating the code word to form visual dictionary. These codewords are used to represent images semantically to form visual labels using Bag-of-Features (BoF). The Histogram intersection method is used to measure the distance between input image and the set of images in the image database to retrieve similar images. The experimental results are evaluated over a collection of 1000 generic Web images to demonstrate the effectiveness of the proposed system.
AUTOMATED IMAGE MOSAICING SYSTEM WITH ANALYSIS OVER VARIOUS IMAGE NOISEijcsa
Mosaicing is blending together of several arbitrarily shaped images to form one large balanced image such
that boundaries between the original images are not seen. Image mosaicing creates a large field of view
using of scene and the result image can be used for texture mapping of a 3D environment too. Blended
image has become a wide necessity in images captured from real time sensor devices, bio-medical
equipment, satellite images from space, aerospace, security systems, brain mapping, genetics etc. Idea
behind this work is to automate the Image Mosaicing System so that blending may be fast, easy and
efficient even if large number of images are considered. This work also provides an analysis of blending
over images containing different kinds of distortion and noise which further enhances the quality of the
system and make the system more reliable and robust.
INTRODUCING THE CONCEPT OF BACKINKING AS AN EFFICIENT MODEL FOR DOCUMENT RETR...IJITCA Journal
Today, many institutions and organizations are facing serious problem due to the tremendously increasing size of documents, and this problem is further triggering the storage and retrieval problems due to the continuously growing space and efficiency requirements. This increase in the size and number of documents is becoming a complex problem in most offices. Therefore, there is a demand to address this challenging problem. This can be met by developing a technique to enable specialized document imaging people to use when there is a need for storing documents images. Thus, there is a need for an efficient retrieval technique for this type of information retrieval (IR) systems.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
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Cosmetic shop management system project report.pdf
A Mat Lab built software application for similar image retrieval
1. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE)
e-ISSN: 2278-1676,p-ISSN: 2320-3331, Volume 7, Issue 1 (Jul. - Aug. 2013), PP 113-118
www.iosrjournals.org
www.iosrjournals.org 113 | Page
A Mat Lab built software application for similar image retrieval
Akhilesh Mehta1
, Rakesh Pandey2
, Parul Kansal3
1,2
(Ece M.Tech,Btkit Dwarahat/ Uttarakhand Technical University, India)
3
(Assistant Prof.,Btkit Dwarahat/ Uttarakhand Technical University, India)
Abstract : The paper introduced the algorithm of image retrieval which combine the features of color, shape
and so on , and then we do sorting in the final result. Firstly, the internal and exterior normalization of multi-
features are studied and analyzed, and introduced the normalization algorithm in detail. Secondly, the retrieval
methods of the multi-features are showed. Lastly, adopted the Euclid distance metric, and used the image
database. Our final result is a Mat Lab built software application, with an image database, that utilized shape
and colour features of the images in the database as the basis of comparison and retrieval. The structure of the
final software application is illustrated.
Keywords: image retreival, feature extraction, indexing, edge point detecion, multi-feature, features.mat
I. Introduction
In fact, any the image retrieval based on single feature has certain limitation, it cannot the
comprehensive descript the image contain content [1]. If the multi features of image make in the integration
image retrieval system, the effect of the image retrieval will obtain enormous enhancement [2]. But description
methods such as color, shape had reflected the image feature from the different angle, how does organize these
characteristics, enables them to defer to the user’s request to merge each kind of feature, this is a question which
needs to study
Proposed Algorithm
Figure(1)-Block Diagram of Image Retrieval
II. Image Database
In image database we store all the images in which we want to apply our algorithm.
Figure2. Some images from image database
2. A Mat Lab built software application for similar image retrieval
www.iosrjournals.org 114 | Page
III. FEATURE EXTRACTION AND EDGE DETECTION
AS shown in figure it has two parts. Viz. edge detection and color extraction.
Edge detection: First the RGB image is reduced to thumbnail size and median filtered, and then we use
Four masks as shown in figure(3) to detect edges.
(a)Horizontal (b) Vertical (c) Diagonal (d) Anti-
diagonal Mask Mask Mask Mask
Figure (3)
We have taken an image-
Then the result of four masks is as follow-
Horizontal edges Vertical edges
Diagonal edges Anti-diagonal edges
3. A Mat Lab built software application for similar image retrieval
www.iosrjournals.org 115 | Page
Figure(4)- edge points of images present in database
IV. COLOR EXTRACTION
The color histogram is a method for describing the color content of an image, it counts the number of
occurrences of each color in an image. For color extraction we convert RGB image to HSV image.
RGB to HSV
In order to use a good color model for a specific application, conversion between color models is necessary. A
good color model for an image retrieval system should
preserve the perceived differences in color.
Color quantization
In order to produce color histogram, color quantization is often applied. Color quantization is the process to
reduce the number of colors employed to represent an image. A quantization scheme is determined by the color
model and the segmentation (i.e., split up) of the color model used. As we said before, usually color models
represent a color in the form of tuples (generally of three) .By applying a standard quantization scheme
to a color model, each axis is divided into a certain number of fractions. When the axes are divided into k, l, and
m parts, number (n) of the colors used to represent an image will be: n= k.l.m. A quantization of color model in
n colors is often referred to as a n-bins quantization scheme. We have taken 128bin histogram here.
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Figure(5)- color histogram of images prersent in database
Features.mat: to save time by calculating image content for each query processing we create a file in mat lab,
which saves the edge features and color features of the database.
5.Query processing
Now we have created a features database in which features of all the image of image database are saved.
Now we input a query image. The system first extract the edge feature and color feature of the query image, then
compare it with all the other images present in the database. This is done by using Euclidean distance method.
Thus in last we get images which are similar to the query image.
The features saves in a matrix form as shown in figure and then we use a function of mat lab which is used to
compare the features of images.
D = pdist(X)
It computes the Euclidean distance between pairs of objects in m-by-n data matrix X. Rows of X correspond to
observations, and columns correspond to variables. D is a row vector of length m(m–1)/2, corresponding to
pairs of observations in X. The distances are arranged in the order (2,1), (3,1), ..., (m,1), (3,2), ..., (m,2), ...,
(m,m–1)). D is commonly used as a dissimilarity matrix in clustering or multidimensional scaling.
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Figure(6)- Query image
Now we have the images which are similar to the query image after comparing the features as in array form.
The next step is to sort this to get the best matched image in descending order i.e. at the top we get the images
which are most similar to the query image.
Figure (7) - Final result
V. Conclusion
The paper have analyzed and studied some retrieval questions about multi-feature fusion, for instance
processing in multi- feature vector and so on. Finally uses the Euclidean space in the image database to retrieve
and confirm the multi-features image retrieval method. From the experimental result, we may see that three
kinds of features fuses may obtain the good retrieval effect. We have also created a features.mat file which
contains the features of the image database, which save time of calculating features in every operation. But the
algorithm also has certain insufficiency, for deficiency of detecting emotions such as emotions of human faces
as smile, anger etc. This question is the next research problem.
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