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Low-level Hierarchical Segmentation Statistics of Natural Images
PrePrint
ISSN: 0162-8828
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.
Index Terms:
Markov random field,Natural image statistics,low-level hierarchical segmentation
Citation:
Narendra Ahuja, "Low-level Hierarchical Segmentation Statistics of Natural Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, 04 Feb. 2014. IEEE computer Society Digital Library. IEEE Computer Society, <http://doi.ieeecomputersociety.org/10.1109/TPAMI.2014.2299809>
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