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» Optimizing Learning in Image Retrieval
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JIIS
2002
130views more  JIIS 2002»
13 years 8 months ago
Image Mining: Trends and Developments
Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful informa...
Wynne Hsu, Mong-Li Lee, Ji Zhang
ICPR
2008
IEEE
14 years 3 months ago
Augment document image binarization by learning
Conventional Binarization methods try to obtain optimal results based on the single image only. They make distinct diversity of binarization quality sometimes even for images of t...
Yuanping Zhu
ICIP
2002
IEEE
14 years 10 months ago
On the structure of some phase retrieval algorithms
The state of the art for solving the phase retrieval problem in two dimensions relies heavily on the algorithms proposed by Gerchberg, Saxton, and Fienup. Despite the widespread u...
Heinz H. Bauschke, Patrick L. Combettes, D. Russel...
SIGIR
2009
ACM
14 years 3 months ago
An improved markov random field model for supporting verbose queries
Recent work in supervised learning of term-based retrieval models has shown significantly improved accuracy can often be achieved via better model estimation [2, 10, 11, 17]. In ...
Matthew Lease
ICCV
2003
IEEE
14 years 11 months ago
Learning How to Inpaint from Global Image Statistics
Inpainting is the problem of filling-in holes in images. Considerable progress has been made by techniques that use the immediate boundary of the hole and some prior information o...
Anat Levin, Assaf Zomet, Yair Weiss