Abstract

With the rapid technological growth, availability of digital images in world wide web has increased , since the data levels available in these databases is also increasing enormously, it is very difficult to recognize or retrieve a image of interest instantaneously. Many methodologies have been proposed but still a lot of new technologies are to be proposed in order to retrieve the images of interest basing on the pattern or feature at low cost and with minimum time period. The main disadvantages of the existing methods present in the literature is, the retrieval of images are done with mapping of images using one to one mapping strategy, this method is time consuming ,hence to overcome this disadvantage an attempt is made in this paper . This paper focuses on extracting the image if interest across the web in an efficient manner with the pattern and behavior as the key elements.

References

• “Automatic image annotation Based on Decision Tree Machine learning “Lixing Jiang, JinHou, Dengsheng Zhang
• “Unsupervised image segmentation using finate doubly truncated Gaussian Mixture Model and Hierarchical clustering ” Srinivas Y Journal of Current Science Vol.93 No.4 Aug-2007 PP507-514
• “A review on image segmentation techniques process pattern recognition” Pal N.R, Pal S. K –1993
• “Induction of Decision trees” J.R.QUINLAN Centre for Advanced Computing Sciences, New South Wales Institute of Technology, Sydney 2007,Australia
• “Some studies in machine learning using the game of checkers II: Recent progress.”, Samuel, A. (1967). IBM J. Research and Development 11. balancing poles Michie, 1982,
• "Experiments on the mechanisation of game-learning 2 - Rule-based learning and the human window."Michie, D. (1982). Computer Journal 25
• "A task-independent experience gathering scheme for a problem solver."Quinlan, J.R. (1969). Proceedings of the First International Joint Conference on Artificial Intelligence. Washington, D.C.: Morgan Kaufmann
• "Learning structural descriptions from examples."Winston, P.H. (1975). In P.H. Winston (Ed.), The psychology of computer vision. McGraw-Hill
• “Performance of a reading task by an elementary perceiving and memorizing program," Feigenbaum, E.A., & Simon, H.A. (1963). Behavioral Science, 8.
• H. Almuallim and T. G. Dietterich . Learning with many irrelevant features. In Proc. 9th Nat. Conf. on Artificial Intelligence, volume 2, pages 547–552, Menlo Park, CA, July 1991. AAAI Press.
• L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification of Regression Trees. Wadsworth, 1984.
• W. Buntine and T. Niblett. A further comparison of splitting rules for decision tree induction. Machine Learning, 8:75– 85, 1992.
• U. Fayyad, G. PiatetskyShapiro, and P. Smyth. Knowledge discovery and data mining: Towards a unifying framework. In Proc. 2nd Intl. Conf. on Knowledge Discovery and Data Mining (KDD’96), pages 82–88, Portland, Oregon, 1996.
• U. M. Fayyad. Branching on attribute values in decision tree generation. In Proc. 1994 AAAI Conf., pages 601–606, AAAI Press, 1994.
• U. M. Fayyad, S. G. Djorgovski, and N. Weir. Automating the analysis and cataloging of sky surveys. In U. Fayyad, G. PiatetskyShapiro, P. Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, pages 471–493. AAAI/MIT Press, 1996.