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Multi-Media Modeling Conference, International (2005)
Melbourne, Australia
Jan. 12, 2005 to Jan. 14, 2005
ISSN: 1550-5502
ISBN: 0-7695-2164-9
pp: 239-246
Khanh Vu , University of Central Florida
Kien A. Hua , University of Central Florida
Nualsawat Hiransakolwong , King Mongkut?s Institute of Technology
Sirikunya Nilpanich , University of Central Florida
<p>Image enhancement such as adjusting brightness and contrast is central to improving human visualization of images? content. Images in desired enhanced quality facilitate analysis, interpretation, classification, information exchange, indexing and retrieval. The adjustment process, guided by diverse enhancement objectives and subjective human judgment, often produces various versions of the same image. Despite the preservation of content under these operations, enhanced images are treated as new in most existing techniques via their widely different features. This leads to difficulties in recognition and retrieval of images across application domains and user interest. To allow unrestricted enhancement flexibility, accurate identification of images and their enhanced versions is therefore essential.</p> <p>In this paper, we introduce a measure that theoretically guarantees the identification of all enhanced images originated from one. In our approach, images are represented by points in multidimensional intensity-based space. We show that points representing images of the same content are confined in a well-defined area that can be identified by a so-devised formula. We evaluated our technique on large sets of images from various categories, including medical, satellite, texture, color images and scanned documents. The proposed measure yields an actual recognition rate approaching 100% in all image categories, outperforming other well-known techniques by a wide margin. Our analysis at the same time can serve as a basis for determining the minimum criterion a similarity measure should satisfy. We discuss also how to apply the formula as a similarity measure in existing systems to support general image retrieval.</p>

S. Nilpanich, N. Hiransakolwong, K. Vu and K. A. Hua, "Recognition of Enhanced Images," Multi-Media Modeling Conference, International(MMM), Melbourne, Australia, 2005, pp. 239-246.
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