The document discusses content-based image retrieval (CBIR) which involves retrieving desired images from a large collection based on automatically extracted visual features like color, texture, and shape. It describes using exact Legendre moments to represent images and support vector machines (SVM) to classify images. The algorithm trains each class independently against other classes and constructs hyperplanes to classify new images based on which planes an image's features satisfy. The method achieved over 96% accuracy on a database with features up to order 5 and 18 training images per class.
Fundamental concepts and basic techniques of digital image processing. Algorithms and recent research in image transformation, enhancement, restoration, encoding and description. Fundamentals and basic techniques of pattern recognition.
At the end of this lesson, you should be able to;
describe spatial resolution
describe intensity resolution
identify the effect of aliasing
describe image interpolation
describe relationships among the pixels
At the end of this lesson, you should be able to;
describe the energy and the EM spectrum.
describe image acquisition methods.
discuss image formation model.
express sampling and quantization.
define dynamic range and image representation.
Fundamental concepts and basic techniques of digital image processing. Algorithms and recent research in image transformation, enhancement, restoration, encoding and description. Fundamentals and basic techniques of pattern recognition.
At the end of this lesson, you should be able to;
describe spatial resolution
describe intensity resolution
identify the effect of aliasing
describe image interpolation
describe relationships among the pixels
At the end of this lesson, you should be able to;
describe the energy and the EM spectrum.
describe image acquisition methods.
discuss image formation model.
express sampling and quantization.
define dynamic range and image representation.
Quality and size assessment of quantized images using K-Means++ clusteringjournalBEEI
In this paper, an amended K-Means algorithm called K-Means++ is implemented for color quantization. K-Means++ is an improvement to the K-Means algorithm in order to surmount the random selection of the initial centroids. The main advantage of K-Means++ is the centroids chosen are distributed over the data such that it reduces the sum of squared errors (SSE). K-Means++ algorithm is used to analyze the color distribution of an image and create the color palette for transforming to a better quantized image compared to the standard K-Means algorithm. The tests were conducted on several popular true color images with different numbers of K value: 32, 64, 128, and 256. The results show that K-Means++ clustering algorithm yields higher PSNR values and lower file size compared to K-Means algorithm; 2.58% and 1.05%. It is envisaged that this clustering algorithm will benefit in many applications such as document clustering, market segmentation, image compression and image segmentation because it produces accurate and stable results.
Image Acquisition and Representation
A Simple Image Formation Model
Image Sampling and Quantization
Image Interpolation
Image quantization
Nearest Neighbor Interpolation
COM2304: Introduction to Computer Vision & Image Processing Hemantha Kulathilake
At the end of this lesson, you should be able to;
Describe image.
Describe digital image processing and computer vision.
Compare and Contrast image processing and computer vision.
Describe image sources.
Identify the array of imaging application under the EM Image source.
Describe the components of Image processing system and computer vision system.
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
A STUDY AND ANALYSIS OF DIFFERENT EDGE DETECTION TECHNIQUEScscpconf
In the first study [1], a combination of K-means, watershed segmentation method, and Difference In Strength (DIS) map were used to perform image segmentation and edge detection
tasks. We obtained an initial segmentation based on K-means clustering technique. Starting from this, we used two techniques; the first is watershed technique with new merging
procedures based on mean intensity value to segment the image regions and to detect their boundaries. The second is edge strength technique to obtain accurate edge maps of our images without using watershed method. In this technique: We solved the problem of undesirable over segmentation results produced by the watershed algorithm, when used directly with raw data images. Also, the edge maps we obtained have no broken lines on entire image. In the 2nd study level set methods are used for the implementation of curve/interface evolution under various forces. In the third study the main idea is to detect regions (objects) boundaries, to isolate and extract individual components from a medical image. This is done using an active contours to detect regions in a given image, based on techniques of curve evolution, Mumford–Shah functional for segmentation and level sets. Once we classified our images into different intensity regions based on Markov Random Field. Then we detect regions whose boundaries are not necessarily defined by gradient by minimize an energy of Mumford–Shah functional forsegmentation, where in the level set formulation, the problem becomes a mean-curvature which will stop on the desired boundary. The stopping term does not depend on the gradient of the image as in the classical active contour. The initial curve of level set can be anywhere in the image, and interior contours are automatically detected. The final image segmentation is one
closed boundary per actual region in the image.
At the end of this lesson, you should be able to;
describe Connected Components and Contours in image segmentation.
discuss region based segmentation method.
discuss Region Growing segmentation technique.
discuss Morphological Watersheds segmentation.
discuss Model Based Segmentation.
discuss Motion Segmentation.
implement connected components, flood fill, watershed, template matching and frame difference techniques.
formulate possible mechanisms to propose segmentation methods to solve problems.
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.
Quality and size assessment of quantized images using K-Means++ clusteringjournalBEEI
In this paper, an amended K-Means algorithm called K-Means++ is implemented for color quantization. K-Means++ is an improvement to the K-Means algorithm in order to surmount the random selection of the initial centroids. The main advantage of K-Means++ is the centroids chosen are distributed over the data such that it reduces the sum of squared errors (SSE). K-Means++ algorithm is used to analyze the color distribution of an image and create the color palette for transforming to a better quantized image compared to the standard K-Means algorithm. The tests were conducted on several popular true color images with different numbers of K value: 32, 64, 128, and 256. The results show that K-Means++ clustering algorithm yields higher PSNR values and lower file size compared to K-Means algorithm; 2.58% and 1.05%. It is envisaged that this clustering algorithm will benefit in many applications such as document clustering, market segmentation, image compression and image segmentation because it produces accurate and stable results.
Image Acquisition and Representation
A Simple Image Formation Model
Image Sampling and Quantization
Image Interpolation
Image quantization
Nearest Neighbor Interpolation
COM2304: Introduction to Computer Vision & Image Processing Hemantha Kulathilake
At the end of this lesson, you should be able to;
Describe image.
Describe digital image processing and computer vision.
Compare and Contrast image processing and computer vision.
Describe image sources.
Identify the array of imaging application under the EM Image source.
Describe the components of Image processing system and computer vision system.
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
A STUDY AND ANALYSIS OF DIFFERENT EDGE DETECTION TECHNIQUEScscpconf
In the first study [1], a combination of K-means, watershed segmentation method, and Difference In Strength (DIS) map were used to perform image segmentation and edge detection
tasks. We obtained an initial segmentation based on K-means clustering technique. Starting from this, we used two techniques; the first is watershed technique with new merging
procedures based on mean intensity value to segment the image regions and to detect their boundaries. The second is edge strength technique to obtain accurate edge maps of our images without using watershed method. In this technique: We solved the problem of undesirable over segmentation results produced by the watershed algorithm, when used directly with raw data images. Also, the edge maps we obtained have no broken lines on entire image. In the 2nd study level set methods are used for the implementation of curve/interface evolution under various forces. In the third study the main idea is to detect regions (objects) boundaries, to isolate and extract individual components from a medical image. This is done using an active contours to detect regions in a given image, based on techniques of curve evolution, Mumford–Shah functional for segmentation and level sets. Once we classified our images into different intensity regions based on Markov Random Field. Then we detect regions whose boundaries are not necessarily defined by gradient by minimize an energy of Mumford–Shah functional forsegmentation, where in the level set formulation, the problem becomes a mean-curvature which will stop on the desired boundary. The stopping term does not depend on the gradient of the image as in the classical active contour. The initial curve of level set can be anywhere in the image, and interior contours are automatically detected. The final image segmentation is one
closed boundary per actual region in the image.
At the end of this lesson, you should be able to;
describe Connected Components and Contours in image segmentation.
discuss region based segmentation method.
discuss Region Growing segmentation technique.
discuss Morphological Watersheds segmentation.
discuss Model Based Segmentation.
discuss Motion Segmentation.
implement connected components, flood fill, watershed, template matching and frame difference techniques.
formulate possible mechanisms to propose segmentation methods to solve problems.
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.
Performance Comparison of Image Retrieval Using Fractional Coefficients of Tr...CSCJournals
The thirst of better and faster retrieval techniques has always fuelled to the research in content based image retrieval (CBIR). The paper presents innovative content based image retrieval (CBIR) techniques based on feature vectors as fractional coefficients of transformed images using Discrete Cosine, Walsh, Haar and Kekre’s transforms. Here the advantage of energy compaction of transforms in higher coefficients is taken to greatly reduce the feature vector size per image by taking fractional coefficients of transformed image. The feature vectors are extracted in fourteen different ways from the transformed image, with the first being considering all the coefficients of transformed image and then fourteen reduced coefficients sets (as 50%, 25%, 12.5%, 6.25%, 3.125%, 1.5625% ,0.7813%, 0.39%, 0.195%, 0.097%, 0.048%, 0.024%, 0.012% and 0.06% of complete transformed image) are considered as feature vectors. The four transforms are applied on gray image equivalents and the colour components of images to extract Gray and RGB feature sets respectively. Instead of using all coefficients of transformed images as feature vector for image retrieval, these fourteen reduced coefficients sets for gray as well as RGB feature vectors are used, resulting into better performance and lower computations. The proposed CBIR techniques are implemented on a database having 1000 images spread across 11 categories. For each proposed CBIR technique 55 queries (5 per category) are fired on the database and net average precision and recall are computed for all feature sets per transform. The results have shown performance improvement (higher precision and recall values) with fractional coefficients compared to complete transform of image at reduced computations resulting in faster retrieval. Finally Kekre’s transform surpasses all other discussed transforms in performance with highest precision and recall values for fractional coefficients (6.25% and 3.125% of all coefficients) and computation are lowered by 94.08% as compared to DCT.
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.
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.
Object Shape Representation by Kernel Density Feature Points Estimator cscpconf
This paper introduces an object shape representation using Kernel Density Feature Points
Estimator (KDFPE). In this method we obtain the density of feature points within defined rings
around the centroid of the image. The Kernel Density Feature Points Estimator is then applied to
the vector of the image. KDFPE is invariant to translation, scale and rotation. This method of
image representation shows improved retrieval rate when compared to Density Histogram
Feature Points (DHFP) method. Analytic analysis is done to justify our method and we compared our results with object shape representation by the Density Histogram of Feature Points (DHFP) to prove its robustness.
Performance Evaluation of Object Tracking Technique Based on Position VectorsCSCJournals
In this paper, a novel algorithm for moving object tracking based on position vectors has proposed. The position vector of an object in first frame of a video has been extracted based on selection of region of interest. Based on position vector in first frame object direction has shown in nine different directions. We extract nine position vectors for nine different directions. With these position vectors next frame is cropped into nine blocks. We exploit block matching of the first frame with nine blocks of the next frame in a simple feature space by Descrete wavelet transform and dual tree complex wavelet transform. The matched block is considered as tracked object and its position vector is a reference location for the next successive frame. We describe performance evaluation and algorithm in detail to perform simulation experiments of object tracking using different feature vectors which verifies the tracking algorithm efficiency.
Video surveillance is becoming more and more important forsocial security, law enforcement, social order,military, and other social problems. In order to manage parking information effectively, this vehicle
detection method is presented. In general, motion detection plays an important role in video surveillance
systems. In this paper, firstly this system uses ViBe method to extract the foreground object, then extracts
HOG features on the performance of the ROI of images. At last this paper presents Support vector machine for vehicle recognition. The results of this test show that, the recognition rate of vehicle’s model in this recognition system is up the industrial application standard.
Video surveillance is becoming more and more important forsocial security, law enforcement, social order,military, and other social problems. In order to manage parking information effectively, this vehicle detection method is presented. In general, motion detection plays an important role in video surveillance systems. In this paper, firstly this system uses ViBe method to extract the foreground object, then extracts HOG features on the performance of the ROI of images. At last this paper presents Support vector machine for vehicle recognition. The results of this test show that, the recognition rate of vehicle’s model in this recognition system is up the industrial application standard.
ideo surveillance is becoming more and more important forsocial security, law enforcement, social
order,military, and other social problems. In order to manage parking information effectively, this vehicle
detection method is presented. In general, motion detection plays an important role in video surveillance
systems. In this paper, firstly this system uses ViBe method to extract the foreground object, then extracts
HOG features on the performance of the ROI of images. At last this paper presents Support vector machine
for vehicle recognition. The results of this test show that, the r
Vehicle Recognition Using VIBE and SVMCSEIJJournal
Video surveillance is becoming more and more important forsocial security, law enforcement, social
order,military, and other social problems. In order to manage parking information effectively, this vehicle
detection method is presented. In general, motion detection plays an important role in video surveillance
systems. In this paper, firstly this system uses ViBe method to extract the foreground object, then extracts
HOG features on the performance of the ROI of images. At last this paper presents Support vector machine
for vehicle recognition. The results of this test show that, the recognition rate of vehicle’s model in this
recognition system is up the industrial application standard.
2. Need for image data management
For efficient storage and retrieval of images
in large databases.
While it is perfectly feasible to identify a
desired image from a small collection simply
by browsing, more effective techniques are
needed with collections containing
thousands of items which need some form of
access by image content.
3. What is CBIR?
Process of retrieving desired images from a
large collection on the basis of features (such
as colour, texture and shape) that can be
automatically extracted from the images
themselves.
Also known as query by image content (QBIC)
and content-based visual information retrieval
(CBVIR)
4. Contd…..
“Content-based” means that the
search will analyze the actual contents
of the image.
Indexing is often used as identifying
features within an image.
Indexing data structures: structures to
speed up the retrieval of features within
image collections.
5.
6. Practical applications of CBIR
Crime prevention
The military
Architectural and engineering design
Fashion and interior design
Journalism and advertising
Medical diagnosis
Geographical information and remote sensing systems
Cultural heritage
Education and training
Home entertainment
Web searching.
7. Content comparisons
Color : The size of the feature vector
depends on the size of the image.
Texture: Texture based features do not
describe much about variance and
rotation.
So we have considered shape
features
8. Feature extraction using Exact Legendre
moment computation
image moments :particular weighted
averages of the image pixels'
intensities
or
functions of those moments chosen
to have some attractive property or
interpretation.
Main advantage :ability to provide
invariant measures of shape.
9. Image moments are basically classified
into
a) non-orthogonal moments and
b) orthogonal moments.
Orthogonal moments: representation of
image with minimum amount of
information redundancy
CLASSIFICATION OF IMAGE MOMENTS
10. Legendre moments
Belong to the class of orthogonal
moments
used to attain a near zero value of
redundancy measure in a set of
moment functions
correspond to independent
characteristics of the image.
11. The definition of Legendre moments has a form
of projection of the image intensity function onto
the Legendre polynomials.
Legendre moments of order (p + q) for an image
with intensity function f (x, y) are defined as
Contd….
12. Contd…….
where P(x) is the pth-order Legendre polynomial
defined as
where x [−1, 1], and the Legendre polynomial Pp(x)
obeys the following recursive relation:
with P0(x) = 1, P1(x) = x and p>1.
13. A digital image of size M ×N is an array of
pixels. Centers of these pixels are the
points (xi,yj ), where the image intensity
function is defined only for this discrete set
of points fixed at constant values
Δxi = 2/M, and Δyj = 2/N
in x and y directions repectively.
14. Exact Legendre moments
The integrals in Legendre moments are
evaluated exactly using summations to
reduce the approximation error.
The computation time and
computational complexity are reduced
by applying fast algorithm.
16. Contd…
Exact Legendre moments are computed using fast
algorithm as follows:
Where,
Yiq is the qth order moment of row i.
17. Classification of data classes using
support vector machine (SVM)
SVMs are a set of related supervised
learning methods used for classification .
Viewing input data as two sets of
vectors in an n-dimensional space, an
SVM will construct a separating hyperplane
in that space, one which maximizes the
margin between the two data sets.
18. Contd…..
Margin: two parallel hyperplanes are
constructed, one on each side of the separating
hyperplane, which are "pushed up against" the
two data sets.
Larger the margin, better the generalization
error of the classifier.
19. Objectives
The objectives of SVM are:
To define a optimal hyper plane with
maximum margin.
To map data into high dimensional space to
make it easier for linear classification.
20. A p − 1-dimensional hyper plane
separating p-dimensional data points.
The points of one class are divided from the
other class using this hyper plane
Linear classifiers
24. 24`
Setting Up the Optimization Problem
kbxw −=+⋅
kbxw =+⋅
0=+⋅ bxw
kk
w
The width of the
margin is:
2 k
w
Now we have to
maximize the
margin.
K=1=>
2
max
. . ( ) 1, of class 1
( ) 1, of class 2
w
s t w x b x
w x b x
× + ≥ ∀
× + ≤ − ∀
25. quadratic programming (QP) optimization
problem.
We have to minimize the value of Subjected to certain constraints
This is the primal form
It is expressed in dual form to make it easier to
optimize
Here we obtain non zero Lagrange multipliers.
These are called support vectors.
27. Algorithm
1. Read all the images from the database.
2. The Exact Legendre moments of each
image is calculated.
3.Each class is trained with every other class
independently using SVM.
4. The first class of images is trained with all
the other 19 classes using SVM and 19
different hyper planes are constructed.
28. 5. The first step in training process involves
labeling of the training images. The class that is
considered positive for training is labeled Y= +1
and all other images are labeled Y=-1.
6. A optimized hyper plane is constructed that
divides the positive images from other classes
using SVM.
29. 7. The Hessian matrix is calculated for the set of
training vectors.
H=∑Xi.Xj.ci.cj. where X is the set of feature
vectors.
8. the dual optimization form of the equation is
calculated
9. Using ‘quadprog’ function in Matlab the
optimization of equation is done.
30. 10.there is one weight for every training point
where the points with O< a, < C are called
support vectors. Using these support vectors the
value of W is calculated.
11. The value of bias is obtained from the
equation,
b= w.x-1, where x is a training image
31. ……….so on
Each class is
trained with every
other class and a
hyper plane is
constructed.
32. 12.The feature vectors of a query image are taken
and are substituted in all the planes.
13. The values of the planes are observed.
The image is classified into that class which has
the maximum number of planes satisfied.
34. Experimental work and results
We have taken coil database consisting of 20
different classes of images each class consisting of
72 images.
The different classes of images that were taken in
the database are as shown below:
36. The results show that there has been a linear
growth in the classification percentage with the
number of training images increased.
The feature vectors of the images are increased
by taking higher orders of Legendre moments.
The retrieval rate is found to be 96.592% with
18 images taken for training and legendre
moments upto the order of 5.
40. Future scope
Exact Legendre moments of higher order can
be considered.
Focus on CBIR systems that can make use of
relevance feedback, where the user
progressively refines the search results by
marking images in the results as "relevant",
"not relevant", or "neutral" to the search
query, then repeating the search with the new
information may be done in future.