Superpixel-based online wagging one-class ensemble for feature selection in foreground/background separation

Last Page Update: 19/03/2018

We present a novel online one-class ensemble based on wagging to select suitable features to each region of a certain scene to distinguish the foreground objects from the background. In addition, we propose a mechanism to update the importance of each feature discarding insignificantly features over time.

HIGHLIGHTS

• A novel methodology to select the best features based on wagging.
• A superpixel segmentation strategy to improve the segmentation performance, increasing the computational efficiency of our ensemble.
• A mechanism called Adaptive Importance Computation and Ensemble Pruning (AIC-EP) to suitably update the importance of each feature discarding insignificantly features over time.

BRIEF OVERVIEW OF THE PROPOSED FRAMEWORK

Brief overview of the proposed framework. A set of features are extracted from the training image sequence. Next, our wagging version creates different pools of IWOC-SVM classifiers from a certain feature. A heuristic approach called Small Votes Instance Selection (SVIS) is used in the IWOC-SVM model updating step. Finally, we use a mechanism called Adaptive Importance and Ensemble Pruning (AIC-EP) to update the importance of the classifiers discarding insignificantly classifiers over time. Only the classifiers with high importance are selected and combined to form a strong classifier. The whole framework described here works as incremental manner.

ALGORITHM: THE WAGGING FOR FEATURE SELECTION

BACKGROUND SUBTRACTION RESULTS ON RGB-D DATASET​​​​​​​​​​​​​​

Background subtraction results on RGB-D dataset -- (a) original frame, (b) features map showing most important feature for each region and (c) its respective histogram of features importance.

Citation

If you use this code for your publications, please cite it as:

@inproceedings{silva Caroline
author    = {Silva, Caroline and Bouwmans, Thierry and Frelicot, Carl},
title     = {Superpixel-based incremental wagging one-class ensemble for feature selection in foreground/background separation},
booktitle = {Pattern Recognition Letters (PRL)},
year      = {2017},
url       = hhttps://www.sciencedirect.com/science/article/pii/S0167865517304038}