www.applebuz.com
REPORTON
image mining
CONTENTS Introduction ..................3 Process Of Image
Mining ..................4 Techniques in Image Mining
..................4 Issues in image mining ..................5
Feature Extraction for Image Mining ..................6 Google
Earth as image database .................10 Images Mining
application ..................11 Conclusion ..................11
Abstract Due to the digitization of data and advances in
technology, it has become extremely easy to obtain and store large
quantities of data, particularly Multimedia data. Fields ranging
from Commercial to Military need to analyze these data in an
efficient and fast manner. Presently, tools for mining images are
few and require human intervention. Feature selection and
extraction is the pre-processing step of Image Mining. Obviously
this is a critical step in the entire scenario of Image Mining. Our
approach to mine from Images to extract patterns and derive
knowledge from large collections of images, deals mainly with
identification and extraction of unique features for a particular
domain. Though there are various features available, the aim is to
identify the best features and thereby extract relevant information
from the images. We have tried various methods for extraction; the
features extracted and the techniques used are evaluated for their
contribution to solving the problem. Experimental results show that
the features used are sufficient to identify the patterns from the
Images. The extracted features were evaluated for goodness and
tested on test images. An interactive system was developed which
allows the user to define new features and to resolve uncertain
regions. We can get much knowledge from images. Our goal is to
achieve a middle-level understanding of image semantics to bridge
the semantic gap existing in the field of image mining and
retrieval With the help of a popular search engine.Introduction
Image mining [1] deals with extraction of implicit knowledge,
image data relationship or other patterns not explicitly stored in
images and uses ideas from computer vision, image processing, image
retrieval, data mining, machine learning, databases and AI. The
fundamental challenge in image mining is to determine how
low-level, pixel representation contained in an image or an image
sequence can be effectively and efficiently processed to identify
high-level spatial objects and relationships. Typical image mining
process involves preprocessing ,transformations and feature
extraction, mining (to discover significant patterns out of
extracted features), evaluation and interpretation and obtaining
the final knowledge. Various techniques from existing domains are
also applied to image mining and include object recognition,
learning, clustering and classification, just to name a few.
Association rule mining is a well-known data mining technique that
aims to find interesting patterns in very large databases. Some
preliminary work has been done to apply association rule mining on
sets of images to find interesting patterns .
Vast amount of image data are generated every day such as
medical images, satellite images, etc.,
Analysis of these images can reveal useful information to the
users.
To analyze the collection of images, image mining concepts are
used. Image mining is extension of data mining to image domain.
Image mining deals with extraction of implicit knowledge from
large collection of images. That is not explicitly stored in the
image. However there is a general agreement that sufficient tools
are not available for analysis of images. One of the issues is the
effective identification of features in the images and the other
one is extracting them. One of the difficult task is knowing the
image domain and obtaining a priori knowledge of what information
is required from the image. This is one of the reasons the Image
Mining process cannot be completely automated.
Extracting association rules from images remains a complex,
tedious process. It typically requires writing several hundred
lines of code to read images, extract features and apply a mining
algorithm such as APRIORI [2]. iARM is a scripting language that
makes it easier to extract association rules from images. It allows
defining a list of source image files and customizing association
rule parameters. These parameters include number of terms, filters
on text feature and configuration of signal features (i.e. color),
support and confidence. Using iARM, association rules can be
extracted by writing simple, easy to understand code that can be
written and maintained by end users (i.e. knowledge workers) with
no programming knowledge.
Image mining have led to tremendous growth in significantly
large and detailed image databases. The most important areas
belonging to image mining are: image knowledge extraction,
content-based image retrieval, video retrieval, video sequence
analysis, change detection, model learning, as well as object
recognition. Two different types of input data for knowledge
extraction from an image collection are original image and symbolic
description of the image .Process Of Image Mining
Image Processing principal component analysis
texture feature extraction
classification & clustering
Databases object-oriented database
image database
Graphical User Interface query
browsing
visualization
Techniques in Image Mining: 1. New Mathematical Techniques in
Image Mining: Algebraic Approaches Discrete Mathematics Techniques
Structural and Syntactic Techniques Multiple Classifiers Other
Mathematical
Techniques2.ImageModelsandImageFeatures3.AutomationofImageMining:-ImageMining,ComputerVisionandKnowledge-BasedSystems-ImageDatabases.ImageKnowledgeBases-ImageMiningTechnologies
-LinguisticTools:-ImageScienceOntologie
Issues in image mining
The various issued in image mining are [18]:Image Mining for
Modeling of Forest Fires From Meteosat Images
1. Stochastic methods for image mining and data quality
(DAQUAL)
2. In agricultural studies, topics like precision agriculture
and crop modeling will be addressed
3. In environmental studies, the topic of spatial/temporal
scales is still an ongoing issue for research.
4. Health issues concern the quantitative modeling of epidemics
of a various kind.
5. Hydrology focuses on model-based Geostatistics for rainfall
prediction.
The increasing number of image archives has made image mining an
important task because of its potential to discove useful image
patterns and relationships from a large set of images. A framework
for extracting knowledge from a sequence of images has been
proposed by Hsu , Lee and Goh. The structure of the framework
composed of two modules: image analysis and knowledge processing
[19].Feature Extraction for Image MiningIn general, images have the
following features color, texture, shape, edge, shadows,
temporal
details etc. The features that were
most promising were color, texture and edge. The reasons are as
follows:
1. . Color: Egeria occurs in 2 colors pink (rusty rose) and
black.
elements can be compared to these spectra. 2. Texture: Texture
is defined as a neighborhood feature [RHC99] as a region or a
block
3. . Edge: Edge is simply a large change in frequency. This is
particularly important here, as the distinction between the dark
Egeria and the lighter water bodies or land can be considered as an
edge.Color Feature Extraction:Some of the techniques tried were
Average color in Gray scale, Average color in RGB.Average color =
------------------------------------------------------------------------
(totalpixelsin theblock)format [GW92] and Average color in YCBCR
(Y is the luminance and CB, CR are thechrominance components)
[GW92]. We evaluated the various methods using Precision andRecall
(introduced in the next section which compares the Precision and
Recall values of themethods), and found that YCBCR performs better
than the other two. Hence we used it as thebasis of color
extraction as shown in the image below (1000_2m_lvi2.tif).
(Intensity of all pixies in the current block) Confusion
MatrixThe confusion matrix is a table structure, which describesall
possible outcomes of a prediction result. The possible outcomes of
a two-class prediction can be True Positive (TP), True Negative
(TN), False Positive (FP) and False Negative (FN).
True positives and True Negatives are the correctly classified
instances. A false positive is when the outcome is incorrectly
classified as positive (yes) when it in fast is negative (no).
Falpositives are the False Alarms in the classification process. A
false negative is when the outcome is incorrectly predicted as
negative when it should have been in fact positive. False negatives
are the instances that the classifier failed to predict. Table 4
shows a two-class confusion matrix.
Predicted
Class
Actual
ClassYesNo
YesTPFN
NoFPTN
Table 4. Confusion MatrixEvaluation criteria
Precision: It is defined as the fraction of the retrieved
information, which is relevant.
Precision= (TP) (TP + FP)
Recall: It is defined as the fraction of the retrieved
information relevant versus all relevant information:
Recall = (TP)
(TP + FN)
Accuracy: It is the overall success rate of the classifier. We
will not be using this criterion, as it does not give a correct
measure for the goodness of the classifier.
Accuracy = (TP+TN)
(TP+TN+FP+FN)
Generality: It can be defined as the ability of the system to
learn well from the training image(s) and then show acceptable
prediction accuracy on the other testing images in general.
Scalability: This is a measure, which tells whether a
classification scheme can be modified so that it could be applied
to larger sized or larger number of images without any noticeable
change in its efficiency or performance.
Reduction in Expert interaction: The aim of this project is to
reduce the load on the experts to manually classify the images
themselves. We say that a classification scheme is good if it
certainly classifies most of the instances on its own and it is
uncertain only for a small fraction of the instances, which are the
only ones classified by the experts manually.
We will be using precision, recall, generality and reduction in
expert interaction as our evaluation criteria. The schemes that
have been chosen here have been extensively modified to achieve
high generality without loosing any information in the training
image itself. As the first set of images did not have a coverage
set, we could not compare the results with the actual classes to
get the TP, TN, FP and FN values and so we could not calculate the
precision and recall values for the testing images. So for the
first set of images we only have generality and reduction in expert
interaction as our evaluation measures (Table 5). For the second
set we can calculate the precision and recall values and so these
values are also appended to the evaluation results (Table 6).
RETRIEVAL BASED ON HIGH LEVEL SEMANTIC FEATURES by high level
color properties
by high level texture properties
by high level shape properties
A set of high level semantic features which are defining in the
image mining process are used. They combine high level color,
texture and shape properties and high level semantic features
defined by the expert during the image mining. In our example for
the query Figure 1. Sea, sky images with a regain with worm color
Figure 2. House images Find a sea or sky images with a regain with
worm color the following images are retrieved (Figure 1.) The
result from the query Find a house images is given in the Figure 2.
In the first query we use color description to find images with
color regions satisfy worm contract and textures of sea and sky. In
the second query we use shape descriptors for the house forms.
Google Earth as image database
other software tools, Image Cutter and Google Map CreatorImage
Shape has 3 options:1) Rectangle - for all images apart from
panoramas, the set Field of View (FOV) button provides options
relating to the camera settings but the most part these can be
ignored as the software will automatically set the correct sizes
and aspect ratio.2) Cylinder - for partial panoramic images or
images that do not cover a full 360 x 180 field of view. The FOV
settings are important for cylindrical images, these should be
noted down via your stitching software and inputted accordingly
3) Panoramic Sphere - for use with 360 x180 degree panorama
images.
Image Mining applications medical/biological images (training;
research)
biometrics/security
satellite image analysis
photo collections
museum images
logos
ConclusionVery soon after the introduction of the notion of
data-mining in the nineties, it became clear that knowledge
discovery, a term often used for data mining techniques, was not
just applicable to the digging up of more or less hidden data
patterns in traditional databases. Everyday, the average person
with a computer faces a growing flow of multimedia information,
particularly via the internet. But this ocean of information would
be useless without the ability to manipulate, classify, archive and
access them quickly and selectively. However multimedia is
dominated by images, with respect to bandwidth and complexity.
Media mining refers to a technique whereby a user can retrieve,
organize, and manage media data. Media mining has a huge number of
emerging applications with different usage models.Image mining is
more than just an extension of data mining to image domain. It is
an interdisciplinary endeavor that draws upon expertise in computer
vision, image processing, image retrieval, data mining, machine
learning, database, and artificial intelligence. Image mining have
led to tremendous growth in significantly large and detailed image
databases. The increasing number of image archives has made image
mining an important task because of its potential to discover
useful image patterns and relationships from a large set of images.
The authors are exploring the image mining in depth in order to
propose algorithms for improving the efficiency and effectiveness
of image mining. EMBED Unknown
Page 2
_1331126050.vsdData
Data
Graphical User Interface
Browsing / Query
Clustering
Land Cover Classification
Texture Feature Extraction
Image Database
Object-Oriented Database
PCA
Image Repository
Image Processing Module
Databases Module
