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\title{Uncertainty Analysis for Spatial Image Extractions in the Context of Ontology and Fuzzy C-Means Algorithm}
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             \author[1]{Dr. Md.Sarwar  kamal}

             \author[2]{Sonia Farhana  Nimmy}

             \author[3]{Linkon  Chowdhury}

             \affil[1]{  BGC Trust Uiversity Bangladesh}

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\date{\small \em Received: 14 February 2012 Accepted: 5 March 2012 Published: 15 March 2012}

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\begin{abstract}
        


This paper emphasis on spatial feature extractions and selection techniques adopted in content based image retrieval that uses the visual content of a still image to search for similar images in large scale image databases, according to a user?s interest. The content based image retrieval problem is motivated by the need to search the exponentially increasing space of image databases efficiently and effectively. It is also possible to classify the remotely sensed image to represent the specific feature of the target images. In this research we first imposed the Fuzzy C-means algorithm to our sample image and observed its value. After getting the experimental result from Fuzzy C-means we have had designed Ontological Matching algorithm which aftereffect better than the previous one. We have had espy that our Ontological Matching algorithm is twenty (20%) percent better than Fuzzy C-means algorithm.

\end{abstract}


\keywords{Ontological Matching Algorithm, Fuzzy C-means algorithm, Spatial Feature Extractions, feature selection, Fuzzy Logic.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
he increase in computing power and electronic storage capacity has lead to an exponential increase of digital content available to users in the form of images which form the bases of many applications \hyperref[b0]{[1]}. Consequently, the search for the relevant information in the large space of image databases has become more challenging. How to manage appropriate extracted outcome is still difficult problem and it is a proper field to make experiment. A typical image retrieval system includes feature extraction usually in conjunction with feature selection \hyperref[b1]{[2]}. We can depict any image as a collection of color, texture and shape features. While several image retrieval systems rely on only one feature for the extraction of relevant images, but exact collection of relevant features can yield better retrieval performance \hyperref[b2]{[3]}. The process of determining the combination of features that is most representative of a particular query image is called feature selection.\par
In case of analyzing real-world maps, the images shown there may not distinctly identify accurate and comprehensible information; rather lots of knowledge may be embedded in the domain in a hidden and unexplored form. 
\section[{a) Fuzzy Logic}]{a) Fuzzy Logic}\par
The logic which works with approximation instead of exact and constant value is called fuzzy logic. The logic has been used from long back to solve various problem domains. The working value of fuzzy logic can be any value in between 0 and 1.Although the fuzzy logic is relatively young theory, the areas of applications are very wide: process control, management and decision making, operations research, economies and, for this paper the most important, pattern recognition and classification. An idea to solve the problem of image classification in fuzzy logic manner as well as comparison of the results of supervised and fuzzy classification was the main motivation of this work. Where, U M fcn, , V = (v 1 ,v 2 ,?,v c ), v i R p is the i th prototype m>1 is the fuzzifier and The objective is to find that U and V which minimize J m The Steps fro FCM Algorithm: 1. Choose: 1 < c < n, 1 < m < , = tolerance, max iteration = N 2. Calculation of membership values as according to the equation (  {\ref 2} ) , ( J 
\section[{II.}]{II.} 
\section[{Fuzzy c-means algorithm}]{Fuzzy c-means algorithm}ik c i n k m ik m D u U ?? ? ? ? V j i D D u c k m ik ij ij , 1 1 1 2 ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? 2 2 k i ik D v x ? ? ? ? ? ? 3.\par
Computer the centroids values according to the equation (\par
(3)\par
4. Selection of new multiplier fields. 5. Repeat the step 2 until the algorithm has converged.\par
III. 
\section[{Fuzzy matching}]{Fuzzy matching}\par
Let us consider the fuzzy matching for the mixing images on the input images \hyperref[b9]{[10]}. .The degree to which the input target images satisfy the conditions of fuzzy rules and conditions .Suppose IMAGE X is defined by rules R1 and IMAGES Y is defined by rules R2.In this case the matching degree will be represented by as follows:\par
Matching Degree (IMAGE X,R1)=?(IMAGE X) 
\section[{Matching Degree (IMAGE Y,R2) =? (IMAGE Y)}]{Matching Degree (IMAGE Y,R2) =? (IMAGE Y)}\par
Where ? is the fuzzy membership function.\par
The fuzzy matching determines the actual outcome for fuzzy optimization which is accomplished here by fuzzy matrix. Here is a graphical view of fuzzy matching degree for IMAGE Y as follows:  
\section[{Classification procedure}]{Classification procedure}\par
In our previous work we have done the classification by projecting the maximum classifier without NULL classifier is used. We implied a normal distribution and evaluate the variance and correlation of spectral response during the classification of the unknown pixel.\par
Here we have had fixed the partitioning as follows:\par
Let we have a data set X = \{x 1 , x 2 ?., x n \} R p and A classification of X is a c n matrix U = [U 1 U 2\par
?U n ] = [u ik ], where U n denotes the k-th column of U.\par
We have found three classifications efficient and suitable for our research activity. The labeled vectors for these classifications are: 1. N pc = \{ y R c : y i [0 1] i, y i > 0 i\} Possibility Label 2. N fc = \{y N pc : y i =1\} Fuzzy Label 3. N hc =\{y N fc : y i \{0 ,1\} i \} Hard Label\par
The Fuzzy classification = V. 
\section[{Ontology and knowledge base}]{Ontology and knowledge base}\par
According to Ehrig (2007), ontology contains core ontology, logical mappings, a knowledge base, and a lexicon \hyperref[b2]{[3]}. Core ontology, S, is defined as a tuple of five sets: concepts, concept hierarchy or taxonomy, properties, property hierarchy, and concept to property function. S = (C, ?c R, ? ,? R) \textunderscore  two disjointi u u n k m ik n k k m ik i ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? 1 1 x v µ 1 IMAGE Y µ 1 IMAGE X Best Degree of match=0.99 Degree of match=0.01 Poor ? ? ? ? ? ? ï?" ? ? ? ? ? ? k N M U M fc k pcn fcn ? ? ? ? U : ? ? C ? ? C ? F 2012 
\section[{June}]{June}\par
where C and R are two disjoint sets called concepts" and relations" respectively. A relation is also known as a property of a concept. A function represented by ?(r) =< dom(r); ran(r) > where r ? R, domain is dom(r) and range is ran(r). A partial order ?R represents on R, called relation hierarchy, where r 1 ?R r 2 iff dom (r 1 ) ? C dom (r 2 ) and ran (r 1 ) ? C ran (r 2 ). The notation ? C represents a partial order on C, called concept hierarchy or taxonomy". In a taxonomy, if c 1 < C c 2 for c 1 ; c 2 ? C , then c 1 is a sub concept of c 2 , and c 2 is a super concept of c 1 . If c1 < C c 2 and there is no c 3 ? C with c1 < C c 3 < C c 2 , then c1 is a direct sub concept of c 2 , and c 2 is a direct super concept of c 1 denoted by c 1 c 2 . The core ontology formalizes the intentional aspects of a domain. The extensional aspects are provided by knowledge bases, which contain asserts about instances of the concepts and relations. A knowledge base is a structure KB = (C,R, I, C, , R) consisting of? ? ? VI. 
\section[{Ontological instance matching algorithom}]{Ontological instance matching algorithom}\par
The operational block of the instance matching integrates ontology alignment, retrieves semantic link clouds of an instance in ontology and measures the terminological and structural similarities to produce matched instance pairs. Pseudo code of the Instance Matching algorithm  
\section[{Experiments with real-world data}]{Experiments with real-world data}\par
For the procedures of image classification was used to gather images from "Google Earth" on the Bangladesh region (Chittagong zone).). It uses this as a case study for implementing feature extraction. The collected images contain some common features such as roads, water, field, agriculture, buildings. The features will be separated based on the pixel intensity value selected for the individual features. It has been chosen as an application area because number of spatial features can be extracted from the map images of Forestry complex.\par
This image contains three channels recorded in three bands: the first band for green, the second for red and the third for blue. In the figure below, we present a fragment of this image and some statistics for the whole image.\par
Figure  {\ref 2} : Forestry Complex Area After performing thresholding [ 5] based on color intensities defined for each and every feature, the features are higlighted with individual colors. Therefore, the highlighted feature area is clearly distinguished from the background. The thresholding process finally extract number of spatial features from the particular region such as road, water, field, building and forest.    [i]=finput.nextInt(); matrix two[k][j]=finput.nextInt(); (matrixone[m][n]>=matrixtwo[m][n]) System.out.print(" "+matrixone[m][n]);\} X. 
\section[{Result evaluations for fuzzy cmeans Classification}]{Result evaluations for fuzzy cmeans Classification}\par
One way of the result evaluation was through the accuracy assessment. The classification results are compared to the raw image data and the report is created. This process is done during the random sample selection. The idea of the accuracy assessment is: point is highlighted in the sample list and observation \hyperref[b8]{[9]} was done where it is located on the image.\par
The following table shows the mean and standard deviation for the classified classes Creation of the membership functions for the output variables is done in the similar manner. Since this is Sugeno-type inference, constant type of output variable fits the best to the given set of outputs (land classes). When the variables have been named and the membership functions have appropriate shapes and names, everything is ready for writing down the rules.\par
Based on the descriptions of the input (green, red and blue channels) and output variables (water, agriculture, forest, buildings, and roads), the rule statements can be constructed: Rules for image classification procedure in verbose format are as follows:  IF (GREEN is 
\section[{Accuracy assessments by ontological classification}]{Accuracy assessments by ontological classification}\par
Idea for accuracy assessment of ontological classification results comes from the manner the maximum likelihood accuracy assessment was performed: select random sample areas with known classes and then let fuzzy logic 'say' what these samples are. With 100 random selected samples, results were as following:\par
Correctly classified samples: 92 Misclassified: 08 Accuracy: 92\%\par
The both experiments and observations clearly showed that Fuzzy Logic classification is better than ontological knowledge base classification for Histo for Spatial Feature Extractions. 
\section[{XIV. Discussion and conclusion}]{XIV. Discussion and conclusion}\par
This paper aimed for extracting the spatial features for providing a fundamental abstraction for modeling the structure of maps representing various raster images. The central part of this paper is an established procedure that is carried out for spatial Historical Heritages classification. As the work continues, it tries to implement every part of the procedure so as to establish its effectiveness and efficiency. It involved the use of supervised learning, assigning membership functions and discovery of pattern feature phases for successfully classifying an image. In the knowledge base, it must be well known whether selected sample forest area or water area.  \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-3.png}
\caption{\label{fig_2}Fig. 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-4.png}
\caption{\label{fig_3}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-5.png}
\caption{\label{fig_4}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4}\includegraphics[]{image-6.png}
\caption{\label{fig_6}Figure 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-7.png}
\caption{\label{fig_7}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5}\includegraphics[]{image-8.png}
\caption{\label{fig_8}Figure 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-9.png}
\caption{\label{fig_9}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.02480106100795756\textwidth}P{0.4486737400530504\textwidth}P{0.16909814323607425\textwidth}P{0.20742705570291778\textwidth}}
\tabcellsep \tabcellsep \tabcellsep :\\
\tabcellsep Channel\tabcellsep Mean\tabcellsep Standard\\
\tabcellsep water (from 50 samples)\tabcellsep \tabcellsep Deviation\\
\tabcellsep Green\tabcellsep 63.11\tabcellsep 22.12\\
\tabcellsep Red\tabcellsep 32.01\tabcellsep 18.31\\
\tabcellsep Blue\tabcellsep 47.58\tabcellsep 24.14\\
\tabcellsep Forest (from 75 samples)\tabcellsep \\
\tabcellsep Green\tabcellsep 120.54\tabcellsep 32.31\\
\tabcellsep Red\tabcellsep 43.35\tabcellsep 27.02\\
( D D D D )\tabcellsep \multicolumn{2}{l}{Blue Agriculture (from 50 samples) 33.19}\tabcellsep 25.33\\
\tabcellsep Green\tabcellsep 92.12\tabcellsep 19.31\\
\tabcellsep Red\tabcellsep 98.58\tabcellsep 35.64\\
\tabcellsep Blue\tabcellsep 69.11\tabcellsep 21.65\\
\tabcellsep \multicolumn{2}{l}{Buildings (from 50 samples)}\\
\tabcellsep Green\tabcellsep 71.55\tabcellsep 25.35\\
\tabcellsep Red\tabcellsep 96.25\tabcellsep 21.98\\
\tabcellsep Blue\tabcellsep 48.56\tabcellsep 20.28\\
\tabcellsep Road (from 75 samples)\tabcellsep \\
\tabcellsep Green\tabcellsep 89.32\tabcellsep 19.22\\
\tabcellsep Red\tabcellsep 64.10\tabcellsep 5.63\\
\tabcellsep Blue\tabcellsep 49.54\tabcellsep 18.16\end{longtable} \par
 
\caption{\label{tab_1}}\end{figure}
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\end{document}
