Low-Level Hierarchical Multiscale Segmentation Statistics of Natural Images


Akbas E. , Ahuja N.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, cilt.36, ss.1900-1906, 2014 (SCI İndekslerine Giren Dergi) identifier identifier identifier

  • Cilt numarası: 36 Konu: 9
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1109/tpami.2014.2299809
  • Dergi Adı: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
  • Sayfa Sayısı: ss.1900-1906

Özet

This paper is aimed at obtaining the statistics as a probabilistic model pertaining to the geometric, topological and photometric structure of natural images. The image structure is represented by its segmentation graph derived from the low-level hierarchical multiscale image segmentation. We first estimate the statistics of a number of segmentation graph properties from a large number of images. Our estimates confirm some findings reported in the past work, as well as provide some new ones. We then obtain a Markov random field based model of the segmentation graph which subsumes the observed statistics. To demonstrate the value of the model and the statistics, we show how its use as a prior impacts three applications: image classification, semantic image segmentation and object detection.