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BMTDL for Scene Modeling on the SBI Dataset

  • Nicoletta NocetiEmail author
  • Alessandra Staglianò
  • Alessandro Verri
  • Francesca Odone
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9281)

Abstract

In this paper we evaluate our method for Background Modeling Through Dictionary Learning (BMTDL) and sparse coding on the recently proposed Scene Background Initialization (SBI) dataset. The BMTDL, originally proposed in [1] for the specific purpose of detecting the foreground of a scene, leverages on the availability of long time observations, where we can treat foreground objects as noise. The SBI dataset refers to more general scene modeling problems – as for video segmentation, compression or editing – where video sequences may be generally short, and often include foreground objects occupying a large portion on the image for the majority of the sequence. The experimental analysis we report is very promising and show how the BMTDL may be also appropriate for these different and challenging conditions.

Keywords

Video Sequence Background Model Image Patch Sparse Code Foreground Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Nicoletta Noceti
    • 1
    Email author
  • Alessandra Staglianò
    • 1
  • Alessandro Verri
    • 1
  • Francesca Odone
    • 1
  1. 1.DIBRISUniversità Degli Studi di GenovaGenovaItaly

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