This article describes about an object interactive System utilizing similarity metric. This measures the highest similar values between images in the database as image captured by webcam. The main objective is to implement a system using the metric of normalized mutual information, supported by an image processing architecture. The main part of this work is the extracting the pixels of captured image where the similarity in images is measured all the image intensity pixel values specified by a region of interest on the images. Assumptions are made for the implementation of the system after considering possible object recognition problems and constraints encountered in the real situation to retrieve the information. The system will also provide additional information regarding the objects. This system also works on internet to retrieve the object information

1. Y. Rubner, C. Tomasi and L. J. Guibas, "A metric for distribution with applications to image databases," in Proc. 1998 IEEE Int. Conf. Computer Vision, Bombay, India, 1998.
2. J. R. Smith and S. -F. Chang, "Single color extraction and image query, b" in Proc. IEEE Int. Conf. Image Processing, Washington, DC, Oct. 1995.
3. F. Mokhtarian and A. K. Mackworth. Scale-based description and recognition of planar curves and two-dimensional shapes. IEEE Trans. Patt. Anal. Machine Intell. , Vol. PAMI-8, pp. 34-43, 1986.
4. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 26, NO. 12, DECEMBER 2004 Shape-Based Recognition of Wiry Objects Owen Carmichael, Member, IEEE, and Martial Hebert, Senior Member, IEEE
5. Y. Chen and E. K. Wong, "Augmented image histogram for image and video similarity search," Proc. SPIE, Jan. 1999
6. Z. Zhou. Recognition of objects shapes based on corner features. Computer Engineering, 33(6):22-26, 2007
7. Belongie, S. , Malik, J. , Puzicha, J. : Shape matching and object recognition using shape contexts. IEEE Trans. on Pattern Analysis and Machine Intelligence 24 (2002) 509
8. Hyung-Bok Kim1 and Kwee-Bo Sim2 "A Particular Object Tracking in an Environment of Multiple Moving Objects"International Conference on Control, Automation and Systems 2010 Oct. 27-30, 2010 in KINTEX, Gyeonggi-do, Korea
9. Fadzliana Saad #1, Rainer Stotzka *"Implementation of an Object Recognition Algorithm Using Normalized Mutual Information" Universiti Teknologi MARA, 40450, Shah Alam, Malaysia.
10. J. Zhang, X. Zhang, H. Krim and G. G. Walter, Object representation and recognition in shape spaces, systern Recognition, vol 36, pp. 1143-1154, 2003.
11. Sande, T. Gevers, and C. Snoek, "Evaluation of color descriptors for object and scene recognition," Proc CVPR, 2008.
12. S. Belongie, J. Malik, and J. Puzicha, "Shape Matching and Object Recognition Using Shape Contexts," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 4, pp. 509-522, Apr. 2002.
13. P. Viola and M. Jones, "Robust Real-Time Object Detection,"Technical Report CRL 2001/01, Compaq Cambridge Research Laboratory, 2001.
14. A Computational Approach to Edge Detection – John Canny, IEEE, 1986.
15. Using The Canny Edge Detector for Feature Extraction and Enhancement of Remote Sensing Images - Mohamed Ali David Clausi, Systems Design Engineering, University of Waterloo, IEEE 2001.
16. A Survey and Evaluation of Edge Detection Operators Application to Medical Images – Hanene Trichili, Mohamed-Salim Bouhlel, Nabil Derbel, Lotfi Kamoun, IEEE, 2002

Rgb Color Extraction Object Recognition Edge Detection Gray Scale Image.