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\title{Color Matching of Images by using Minkowski-Form Distance}
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\begin{document}

             \author[1]{Dr.AJAY B.  KURHE}

             \author[2]{Dr.AJAY B.  KURHE}

             \affil[1]{  Swami Ramanand Teerth Marathwada University, Nanded}

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\date{\small \em Received: 7 February 2011 Accepted: 6 March 2011 Published: 20 March 2011}

\maketitle


\begin{abstract}
        


Content-based image retrieval (CBIR) is an important issue in the computer vision community. Color feature is one of the most important visual feature in CBIR. It is very difficult to recognize object from only shape feature because without color a shape of object looks like many other different objects, so there is need of other features like color. Using both features color and shape we can recognize object efficiently. Color histogram is widely used for image indexing in content-based image retrieval (CBIR). In this paper, we propose color histogram for different eight colors i.e. Black, White, Red, Green, Blue, Yellow, Magenta and Cyan to increase the efficiency of proposed algorithm. The distance between different histogram of the query image with the corresponding histogram of database images are calculated by using Minkowski-Form Distance. Experiment results prove that the CBIR using our new measure has better performance.

\end{abstract}


\keywords{Content-based image retrieval, Minkowski-Form Distance, histogram, color feature.}

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\textcolor{GJBlue}{\footnotesize \\ Artificial Intelligence formulated this projection for compatibility purposes from the original article published at Global Journals. However, this technology is currently in beta. \emph{Therefore, kindly ignore odd layouts, missed formulae, text, tables, or figures.}}
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\let\tabcellsep& 	 	 		 
\section[{INTRODUCTION}]{INTRODUCTION}\par
As processors become increasingly powerful, and memories become increasingly cheaper, the deployment of large image databases for a variety of applications have now become realizable. Databases of art works, satellite and medical imagery have been attracting more and more users in various professional fields like geography, medicine, architecture, advertising, design, fashion, and publishing. Effectively and efficiently accessing desired images from large and varied image databases is now a necessity. One of the most important features that make possible the recognition of images by humans is color. Color is a property that depends on the reflection of light to the eye and the processing of that information in the brain. Usually colors are defined in three dimensional color spaces. These could either be RGB (Red, Green, and Blue), NTSC, YCBCR, HSV (Hue, Saturation, and Value) or HSB (Hue, Saturation, and Brightness). The last two are dependent on the human perception of hue, saturation, and brightness. Due to advances in internet and image databases, Content Based Image Retrieval (CBIR) has become a challenging research area. In CBIR paradigm \hyperref[b0]{[1]} images are automatically indexed using visual contents of an image which are mainly color, texture and shape. These visual features are useful in characterizing an image \hyperref[b1]{[2]} although they can't capture any semantic information of an image. Contentbased image retrieval plays a central role in the application areas such as multimedia database systems in recent years. The work focused on using low-level features like color, texture, shape and spatial layout for image representation. Among all the visual features, color is perhaps the most distinguishing one in many applications. It may be represented by a color histogram \hyperref[b2]{[3]}, color moments \hyperref[b3]{[4]}, color correlogram \hyperref[b4]{[5]}.\par
In this work we are used Micro-Soft image database. This image database free available for academic and research purpose only, we mentioned in references [6] the link of(also segmented or pixel labeled) image database. 
\section[{II.}]{II.} 
\section[{Minkowski-Form Distance}]{Minkowski-Form Distance}\par
If each dimension or image features vector is independent of each other and is of equal importance, the Minkowski-form distance Lp is appropriate for calculating the distance between two images., Let D(I, J) be the distance measure between the query image I and the image J in the database; and fi(I) as the number of pixels in bin i of I .This distance is defined as:D (I,J) = (?| fi (I) -fi(J)| p ) 1/p (1)\par
When p=1, 2,?.. ?, D(I, J) is the L1, L2 (also called Euclidean distance and L? distance respectively. Minkowski-form distance is the mot widely used metric for image retrieval. F. Your Sub Section heading here here here b) Experimental Results\par
For this experiment we used Micro Soft research database which is free for academic research purpose. This database also contains pixel labeled images.\par
To evaluate the test results we use the values precision rate and recall rate are defined as follows precision = number of relevant images selected / total number of retrieved images recall = number of relevant images selected / total number of similar images in the database.   
\section[{CONCLUSION}]{CONCLUSION}\par
Here we propose eight bin histogram of eight colors for retrieve similar color images using Minkowski-Form Distance. Also we can use more than eight colors but the computation is slow when we increase color. There are different techniques for calculating histogram as mentioned above. HSV color space is similar to human perception color system so here we propose HSV color space for histogram. In place of Minkowski distance we can use Quadratic Distance approach.\begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_0}©2011Fig. 1}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Fig. 3}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.69768\textwidth}P{0.15231999999999998\textwidth}}
\multicolumn{2}{l}{UGC sanctioned fund for this work}\\
\multicolumn{2}{l}{(Telephone: 09975644633 E-mail: ajay.kurhe@rediffmail.com)}\\
About\tabcellsep -Asst. Professor in Art, Commerce and Science College, ?\\
\multicolumn{2}{l}{Gangakhed, as a Head of Computer Science Department}\\
\multicolumn{2}{l}{from last 13 years.}\\
\multicolumn{2}{l}{(Telephone:09860057149 E-mail: prb\textunderscore suhas@rediffmail.com)}\\
About\tabcellsep ? -Associate Professor in Dnyanopasak College, Parbhani,\\
\multicolumn{2}{l}{Maharashtra State, India, as Heaad of Computer Science Department}\\
\multicolumn{2}{l}{from 20 years. I got Major Research Project for "visual Perception",}\\
\multicolumn{2}{l}{from University Grant Commission, India. I have completed my}\\
\multicolumn{2}{l}{M.Phil. in 2002 and Ph.D.in 2006 on fuzzy logic which is tool of soft}\\
\multicolumn{2}{l}{computing}\end{longtable} \par
 
\caption{\label{tab_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{}
\end{longtable} \par
  {\small\itshape [Note: IV.]} 
\caption{\label{tab_1}Table 1}\end{figure}
 			\footnote{Color Matching of Images by using Minkowski-Form DistanceFig.2 Block Diagram of Image Retrieval system ©2011 Global Journals Inc. (US)} 			\footnote{Color Matching of Images by using Minkowski-Form Distance Global Journals Inc. (US) Guidelines Handbook 2011 www.GlobalJournals.org} 		 		\backmatter  			 
\subsection[{Retrived Images}]{Retrived Images}\par
Difference with Query Image 1 st 36800 pixels 2 nd 39206 pixels 3 rd 39934 pixels 4 th 43254 pixels 5 th 43816 pixels			 			  				\begin{bibitemlist}{1}
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\bibitem[Domke and Aloimonos (2006)]{b9}\label{b9} 	 		‘Deformation and viewpoint invariant color histograms’.  		 			J Domke 		,  		 			Y Aloimonos 		.  	 	 		\textit{Proc. BMVC (British Machine Vision Conference)},  				 (BMVC (British Machine Vision Conference)Edinburgh, UK)  		September 2006.  	 
\bibitem[Dr.Fuhuri Long,Dr.Hongjiang and Prof.David Dagan Feng (ed.) ()]{b5}\label{b5} 	 		\textit{Fundamental of contentbasedImageRetrieval},  		 \url{http://www.cse.iitd.ernet.in/\textasciitilde pkalra/siv864/Projects/ch01\textunderscore Long\textunderscore v40-proof.pdf}  		Dr.Fuhuri Long,Dr.Hongjiang and Prof.David Dagan Feng (ed.)  		2003. 1.  	 
\bibitem[Han and Ma (2002)]{b10}\label{b10} 	 		‘Fuzzy Color Histogram and Its Use in Color Image Retrieval’.  		 			Ju Han 		,  		 			Kai-Kuang Ma 		.  	 	 		\textit{IEEE Transactions on image Processing}  		August 2002. 11  (8) .  	 
\bibitem[Suhasini et al. (2004)]{b8}\label{b8} 	 		‘Graph Based Segmentation in Content Based Image Retrieval’.  		 			P S Suhasini 		,  		 			K Sri Rama Krishna 		,  		 			I V Krishna 		.  	 	 		\textit{Proceedings of 2004 International Symposium},  				 			Won-Bae Song,  			Dong-Woo Park,  			Jae-Hyeong Kim,  			Ahn 		 (ed.)  		 (2004 International Symposium)  		2008. 2008. 18-19 Nov. 2004. 4 p. .  	 	 (Journal of Computer Science) 
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\end{bibitemlist}
 			 		 	 
\end{document}
