Evaluation Strategies for Automatic Linguistic Indexing of
James Z. Wang, Jia Li, Sui Ching Lin
The Pennsylvania State University, University Park, PA 16802
With the rapid technological advances in machine learning and data
mining, it is now possible to train computers with hundreds of
semantic concepts for the purpose of annotating images automatically
using keywords and textual descriptions. We have developed a system,
the Automatic Linguistic Indexing of Pictures (ALIP) system, using a
2-D multiresolution hidden Markov model. The evaluation of such
approaches opens up challenges and interesting research questions.
The goals of linguistic indexing are often different from those of
other fields including image retrieval, image classification, and
computer vision. In many application domains, computer programs that
can provide semantically relevant keyword annotations are desired,
even if the predicted annotations are different from those of the gold
standard. In this paper, we discuss evaluation strategies for
automatic linguistic indexing of pictures. We provide both objective
and subjective evaluation methods. Finally, we report experimental
results using our ALIP system.
Full Paper in Color
James Z. Wang, Jia Li and Sui Ching Lin, ``Evaluation Strategies for
Automatic Linguistic Indexing of Pictures,'' Proc. IEEE International
Conference on Image Processing (ICIP), vol. 3, pp. 617-620, Barcelona,
Spain, IEEE, September 2003.
Copyright 2003 IEEE.
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May 9, 2003