Image Classification into Object / Non-object Classes
We propose a method that automatically classifies the images into the object and non-object images. An object image is an image with object(s). An object in an image is defined as a set of regions located near the center of the image, which has significant color distribution compared with its surrounding (or background) region. We define three measures for the classification based on the characteristics of an object. The center significance is calculated from the difference in color distribution between the center area and its surrounding region. Second measure is the variance of significantly correlated colors in the image plane. Significantly correlated colors are first defined as the colors of two adjacent pixels that appear more frequently around center of an image rather than at the background of the image. The last one is the edge strength at the boundary of the region that is estimated as an object. To classify the images we combine each measure by training the neural network. A test with 900 images shows a classification accuracy of 84.2%. We also compare our result with the performance of several other classifiers, Naïve Bayes, Decision Table, and Decision Tree.
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- 2.Szummer, M., Picard, R.W.: Indoor-outdoor image classification. In: IEEE Int’l Workshop Content-Based Access Image Video Databases, pp. 42–51 (1998)Google Scholar
- 7.Huang, J., Kumar, S.R., Mitra, M., Zhu, W.J., Zabih, R.: Image Indexing Using Color Correlograms. In: Proc. Computer Vision and Pattern Recognition, pp. 762–768 (1997)Google Scholar
- 8.Lippmann, R.P.: An introduction to computing with neural nets. IEEE ASSP Magazine, 4–22 (1994)Google Scholar
- 9.Witten, I.H., Frank, E.: Data Mining. Academic Press, London (2000)Google Scholar
- 10.Good, I.J.: The estimation of Probabilities: An Essay on Modern Bayesian Methods. MIT Press, MA (1965)Google Scholar
- 11.Kohavi, R.: The Power of Decision Tables. In: Lavrač, N., Wrobel, S. (eds.) ECML 1995. LNCS, vol. 912, Springer, Heidelberg (1995)Google Scholar
- 12.Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J.: Classification and Regression Trees. Wadsworth, Belmont (1984)Google Scholar