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Real-Time Computerized Annotation of Pictures
Jia Li, James Z. Wang
The Pennsylvania State University, University Park, PA 16802
Developing effective methods for automated annotation
of digital pictures continues to challenge computer scientists.
The capability of annotating pictures by computers can lead to
breakthroughs in a wide range of applications, including Web
image search, online picture-sharing communities, and scientific
experiments. In this work, the authors developed new optimization
and estimation techniques to address two fundamental problems
in machine learning. These new techniques serve as the basis
for the Automatic Linguistic Indexing of Pictures - Real Time
(ALIPR) system of fully automatic and high speed annotation for
online pictures. In particular, the D2-clustering method, in the
same spirit as k-means for vectors, is developed to group objects
represented by bags of weighted vectors. Moreover, a generalized
mixture modeling technique (kernel smoothing as a special
case) for non-vector data is developed using the novel concept of
Hypothetical Local Mapping (HLM). ALIPR has been tested by
thousands of pictures from an Internet photo-sharing site, unrelated
to the source of those pictures used in the training process.
Its performance has also been studied at an online demo site
where arbitrary users provide pictures of their choices and indicate
the correctness of each annotation word. The experimental
results show that a single computer processor can suggest annotation
terms in real-time and with good accuracy.
Full Paper in Color
On-line Info on the Research
Jia Li and James Z. Wang, ``Real-Time Computerized Annotation of Pictures,''
IEEE Transactions on Pattern
Analysis and Machine Intelligence, vol. 30, no. 6, pp. 985-1002,
Copyright 2008 IEEE.
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March 1, 2008