Enhancing Training Collections for Image Annotation: An Instance-Weighted Mixture Modeling Approach
Neela Sawant, James Z. Wang, Jia Li
The Pennsylvania State University, University Park, PA 16802
Tagged Web images provide an abundance of labeled
training examples for visual concept learning. However, the
performance of automatic training data selection is susceptible
to highly inaccurate tags and atypical images. Consequently,
manually curated training datasets are still a preferred choice
for many image annotation systems. This paper introduces
'ARTEMIS' - a scheme to enhance automatic selection of
training images using an instance-weighted mixture modeling
framework. An optimization algorithm is derived that in
addition to mixture parameter estimation learns instance-weights,
essentially adapting to the noise associated with each example.
The mechanism of hypothetical local mapping is evoked so
that data in diverse mathematical forms or modalities can
be cohesively treated as the system maintains tractability in
optimization. Finally, training examples are selected from topranked
images of a likelihood-based image ranking. Experiments
indicate that ARTEMIS exhibits higher resilience to noise
than several baselines for large training data collection. The
performance of ARTEMIS-trained image annotation system is
comparable to using manually curated datasets.
Full Paper in Color
More Information about the Project
The Research Group
Neela Sawant, James Z. Wang and Jia Li, ``Enhancing Training Collections for Image Annotation: An Instance-Weighted Mixture Modeling Approach,'' IEEE Transactions on Image Processing, vol. 22, no. 9, pp. 3562-3577, 2013.
Copyright 2013 IEEE.
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 IEEE.
April 20, 2013