Abstract
In many practical machine learning or data mining applications, unlabeled training examples are readily available but labeled ones are fairly expensive to obtain because labeling the examples require human effort. So, learning with unlabeled data has attracted much attention during the past few years. This paper shows that how such techniques can be helpful in a difficult task, content-based image retrieval, for improving the retrieval performance by exploiting images existing in the database.
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Zhou, ZH. (2006). Learning with Unlabeled Data and Its Application to Image Retrieval. In: Yang, Q., Webb, G. (eds) PRICAI 2006: Trends in Artificial Intelligence. PRICAI 2006. Lecture Notes in Computer Science(), vol 4099. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36668-3_3
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DOI: https://doi.org/10.1007/978-3-540-36668-3_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36667-6
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