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

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.

Full Paper in Color
(PDF, 1.2MB)

On-line Info

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. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the ACM.

Last Modified: July 15, 2007
© 2007