Low-Level Hierarchical Multiscale Segmentation Statistics of Natural Images

Date

2014-09-01

Author

Akbaş, Emre

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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.

Subject Keywords

Natural image statistics, Low-level hierarchical segmentation, Markov random field

URI

https://hdl.handle.net/11511/35441

Journal

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

DOI

https://doi.org/10.1109/tpami.2014.2299809

Collections

Department of Computer Engineering, Article

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Citation Formats

E. Akbaş, “Low-Level Hierarchical Multiscale Segmentation Statistics of Natural Images,” IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, pp. 1900–1906, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/35441.