Abstract
In this paper, we propose an EM based Multiple-Instance learning algorithm for the image classification and indexing. To learn a desired image class, a set of exemplar images are selected by a user. Each example is labeled as conceptual related (positive) or conceptual unrelated (negative) image. A positive image consists of at least one user interested object, and a negative example should not contain any user interested object. By using the proposed learning algorithm, an image classification system can learn the user’s preferred image class from the positive and negative examples. We have built a prototype system to retrieve user desired images. The experimental results show that for only a few times of relearning, a user can use the prototype system to retrieve favor images from the WWW over Internet.
This research was supported in part by the National Science Council under Grant NSC 94-2213-E009-139.
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© 2007 Springer-Verlag Berlin Heidelberg
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Pao, H.T., Xu, Y.Y., Chuang, S.C., Fu, H.C. (2007). Image Classification and Indexing by EM Based Multiple-Instance Learning. In: Qiu, G., Leung, C., Xue, X., Laurini, R. (eds) Advances in Visual Information Systems. VISUAL 2007. Lecture Notes in Computer Science, vol 4781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76414-4_15
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DOI: https://doi.org/10.1007/978-3-540-76414-4_15
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