Learning the Consensus on Visual Quality for
Next-Generation Image Management
Ritendra Datta, Jia Li, and James Z. Wang
The Pennsylvania State University, University Park, PA 16802
Abstract:
While personal and community-based image collections grow by the day,
the demand for novel photo management capabilities grows with
it. Recent research has shown that it is possible to learn the
consensus on visual quality measures such as aesthetics with a
moderate degree of success. Here, we seek to push this performance to
more realistic levels and use it to (a) help select high-quality
pictures from collections, and (b) eliminate low-quality ones,
introducing appropriate performance metrics in each case. To achieve
this, we propose a sequential arrangement of a weighted linear least
squares regressor and a naive Bayes' classifier, applied to a set of
visual features previously found useful for quality
prediction. Experiments on real-world data for these tasks show
promising performance, with signi cant improvements over a previously
proposed SVM-based method.
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Citation:
Ritendra Datta, Jia Li and James Z. Wang, ``Learning the Consensus on
Visual Quality for Next-Generation Image Management,'' Proceedings
of the ACM Multimedia Conference, pp. 533-536, ACM, Augsburg, Germany,
September 2007.
Copyright 2007 ACM.
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Last Modified:
July 15, 2007
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