A Stochastic Late Fusion Approach to Human Action Recognition in Unconstrained Images and Videos

Cheema, Muhammad Shahzad; Eweiwi, Abdalrahman; Bauckhage, Christian

doi:10.1007/978-3-319-11752-2_51

Cited by 2 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ma et al [19,20] assume parametric forms of the merged posterior probability, which take the dependency between scores into consideration, and use the training samples to estimate the parameters. As a recent example, Cheema et al [21] assume that the merged posterior probability is a linear combination of source probabilities. Their method solves a constrained quadratic optimisation problem to decide the optimal weights and can deal with the cases with more than two classes.…”

Section: Introductionmentioning

confidence: 99%

Combining multiple biometric traits with an order-preserving score fusion algorithm

et al. 2016

View full text Add to dashboard Cite

Cite this article as: Yicong Liang, Xiaoqing Ding, Changsong Liu, Jing-Hao Xue, Combining multiple biometric traits with an order-preserving score fusion algorithm, Neurocomputing, http://dx. AbstractMultibiometric systems based on score fusion can effectively combine the discriminative power of multiple biometric traits and overcome the limitations of individual trait, leading to a better performance of biometric authentication. To tackle multiple adverse issues with the established classifierbased or probability-based algorithms, in this paper we propose a novel order-preserving probabilistic score fusion algorithm, Order-Preserving Tree (OPT), by casting the score fusion problem into an optimisation problem with the natural order-preserving constraint. OPT is an algorithm fully non-parametric and widely applicable, not assuming any parametric forms of probabilities or independence among sources, directly estimating the posterior probabilities from maximum likelihood estimation, and exploiting the power of tree-structured ensembles. We demonstrate the effectiveness of our OPT algorithm by comparing it with many widely-used score fusion algorithms on two prevalent multibiometric databases.

show abstract

Section: Introductionmentioning

confidence: 99%