# INTRODUCTION 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 [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 [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 [3], color moments [4], color correlogram [5]. 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. # II. # Minkowski-Form Distance 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) 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 For this experiment we used Micro Soft research database which is free for academic research purpose. This database also contains pixel labeled images. 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. # CONCLUSION 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. 1![Fig.1 Histogram of Minkowski-form distance Where H Q is histogram of query image and H l is histogram of database image[10].](image-2.png "©2011Fig. 1") 3![Fig.3 Retrieved Images and upper side of images shows the difference in pixels](image-3.png "Fig. 3") UGC sanctioned fund for this work(Telephone: 09975644633 E-mail: ajay.kurhe@rediffmail.com)About-Asst. Professor in Art, Commerce and Science College, ?Gangakhed, as a Head of Computer Science Departmentfrom last 13 years.(Telephone:09860057149 E-mail: prb_suhas@rediffmail.com)About? -Associate Professor in Dnyanopasak College, Parbhani,Maharashtra State, India, as Heaad of Computer Science Departmentfrom 20 years. I got Major Research Project for "visual Perception",from University Grant Commission, India. I have completed myM.Phil. in 2002 and Ph.D.in 2006 on fuzzy logic which is tool of softcomputing 1IV. Color Matching of Images by using Minkowski-Form DistanceFig.2 Block Diagram of Image Retrieval system ©2011 Global Journals Inc. (US) Color Matching of Images by using Minkowski-Form Distance Global Journals Inc. (US) Guidelines Handbook 2011 www.GlobalJournals.org ## Retrived Images Difference with Query Image 1 st 36800 pixels 2 nd 39206 pixels 3 rd 39934 pixels 4 th 43254 pixels 5 th 43816 pixels * Content-based image retrieval at the end of the early year AW MSmeulders MWaning SASantini RGupra Jain IEEE Tmnsoctionr on Pattern Amlysiz and Machine Intelligence 22 12 2000 * Content based image retrieval systems VNGudivada VRaghavan IEEE.Comput 28 9 1995 * Content-based image retrieval using new color histogram Young-JunSong Won-BaePark Dong-WooKim Jae-HyeongAhn Proceedings of 2004 International Symposium 2004 International Symposium 18-19 Nov. 2004 Intelligent Signal Processing and Communication Systems * Color image retrieval based on primitives of color moments J.-LShih L.-HChen Vision, Image and Signal Processing 2002 149 * Temporal Color Correlograms for Video Retrieval MRautiainen DDoermann Pattern Recognition, Proceedings of 16th International Conference Aug. 2002 1 * Fundamental of contentbasedImageRetrieval Dr.Fuhuri Long,Dr.Hongjiang and Prof.David Dagan Feng 2003 1 * PSSuhasini KRama Krishna DrI VMurali Krishna "cbir Using Color Histogram Processing Journal of Theoretical and Applied Information Technology www jatit.org Vol6. No1. * An efficient similarity indexing by ordering permutations for Spatial Multi-Resolution images RachidAlaoui MohammedSaid Ouatik El Alaoui Meknassi International Journal of Computer Theory and Engineering 1 3 August, 2009 * Graph Based Segmentation in Content Based Image Retrieval PSSuhasini KSri Rama Krishna IVKrishna Proceedings of 2004 International Symposium Won-BaeSong Dong-WooPark Jae-HyeongKim Ahn 2004 International Symposium 2008. 2008. 18-19 Nov. 2004 4 Journal of Computer Science * Deformation and viewpoint invariant color histograms JDomke YAloimonos Proc. BMVC (British Machine Vision Conference) BMVC (British Machine Vision Conference)Edinburgh, UK September 2006 * Fuzzy Color Histogram and Its Use in Color Image Retrieval JuHan Kai-KuangMa IEEE Transactions on image Processing 11 8 August 2002 * Image retrieval based on fuzzy color histogram processing KKonstantinidis AGasteratos IAndreadis Optics Communications 248 2005