Dependency Modeling

Project Goal

This project addresses the independent assumption issue in fusion process. In the last decade, dependency modeling techniques were developed under a specific distribution of classifiers or by estimating the joint distribution of the posteriors. This project proposes a new framework to model the dependency between features without any assumption on feature/classifier distribution, and overcome the difficulty in estimating the high-dimensional joint density.

Block diagram of linear dependency modeling

Fig 1: Block diagram of linear classifier dependency modeling [2]


Fig 2: Block diagram of linear feature dependency modeling [2]


Related Publications

  1. Andy J Ma and Pong C Yuen, “Reduced Analytic Dependency Modeling: Robust Fusion for Visual Recognition,” International Journal of Computer Vision (IJCV), 2014. [PDF] [Project] [Code]
  2. Andy J Ma, Pong C Yuen, and Jian-Huang Lai, “Linear Dependency Modeling for Classifier Fusion and Feature Combination,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. 35, no. 5, pp. 1135-1148, 2013. [PDF] [Project] [Code]
  3. Andy J Ma and Pong C Yuen, “Reduced Analytical Dependency Modeling for Classier Fusion,” European Conference on Computer Vision (ECCV), 2012. [PDF] [Poster] [Spotlight]
  4. Andy J Ma and Pong C Yuen, “Linear Dependency Modeling for Feature Fusion,” IEEE International Conference on Computer Vision (ICCV), 2011. [PDF] [Poster] [Spotlight]