Unsupervised object discovery via self-organisation

  • Authors:

  • Teemu Kinnunen;Joni-Kristian Kamarainen;Lasse Lensu;Heikki KäLviäInen

  • Affiliations:

  • Department of Media Technology, Aalto University, Finland1http://media.tkk.fi/.1;LUT Kouvola Unit, Kouvola, Finland;Machine Vision and Pattern Recognition Laboratory (MVPR), Department of Information Technology, Lappeenranta University of Technology (LUT), Finland2http://www2.it.lut.fi/mvpr/.2;Machine Vision and Pattern Recognition Laboratory (MVPR), Department of Information Technology, Lappeenranta University of Technology (LUT), Finland2http://www2.it.lut.fi/mvpr/.2

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2012

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Abstract

Object discovery in visual object categorisation (VOC) is the problem of automatically assigning class labels to objects appearing in given images. To achieve state-of-the-art results in this task, a large set of positive and negative training images from publicly available benchmark data sets have been used to train discriminative classification methods. The immediate drawback of these methods is the requirement of a vast amount of labelled data. Therefore, the ultimate challenge for visual object categorisation has been recently exposed: unsupervised object discovery, also called unsupervised VOC (UVOC), where the selection of the number of classes and the assignments of given images to these classes are performed automatically. The problem is very challenging and hitherto only a few methods have been proposed. These methods are based on the popular bag-of-features approach and clustering to automatically form the classes. In this paper, we adopt the self-organising principle and replace clustering with the self-organising map (SOM) algorithm. Our method provides results comparable to the state of the art and its advantages, such as non-sensitivity against codebook histogram normalisation, advocate its usage in unsupervised object discovery.