Justify role of Similarity Diffusion Process in cross-media topic ranking: an empirical evaluation

Justify role of Similarity Diffusion Process in cross-media topic ranking: an empirical evaluation Recently top performing cross-media topic detection employs Similarity Diffusion Process (SDP) to rank the interestingness of topics from a large number of candidates. SDP models the polysemous phenomenon from short and less-constrained user-generated data by assuming the similarities between two multi-media data should be divided into intersected topics. The noise in SDP plays an important role to explain the generation of the similarity. However, it is unclear what kind of noise is more appropriate for different modalities in cross media: SDP under different noises should has the lower false positives when topics are successfully detected. In this paper, we provide an in depth analysis of two types of noises (Poisson and Gaussian) for this task. In the evaluation, we observe that the combination of Poisson noise and topic sizes performs best while Gaussian noise has a faster optimization speed than that of Poisson one. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Justify role of Similarity Diffusion Process in cross-media topic ranking: an empirical evaluation

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media, LLC
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-017-5037-7
Publisher site
See Article on Publisher Site

Abstract

Recently top performing cross-media topic detection employs Similarity Diffusion Process (SDP) to rank the interestingness of topics from a large number of candidates. SDP models the polysemous phenomenon from short and less-constrained user-generated data by assuming the similarities between two multi-media data should be divided into intersected topics. The noise in SDP plays an important role to explain the generation of the similarity. However, it is unclear what kind of noise is more appropriate for different modalities in cross media: SDP under different noises should has the lower false positives when topics are successfully detected. In this paper, we provide an in depth analysis of two types of noises (Poisson and Gaussian) for this task. In the evaluation, we observe that the combination of Poisson noise and topic sizes performs best while Gaussian noise has a faster optimization speed than that of Poisson one.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Aug 11, 2017

References

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