CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2014 vol.36 Issue No.09 - Sept.
Issue No.09 - Sept. (2014 vol.36)
Emre Akbas , Department of Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara,
Narendra Ahuja , Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana,
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.
Image segmentation, Histograms, Computational modeling, Vectors, Image edge detection, Markov processes, Gray-scale,Markov random field, Natural image statistics, low-level hierarchical segmentation
Emre Akbas, Narendra Ahuja, "Low-Level Hierarchical Multiscale Segmentation Statistics of Natural Images", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.36, no. 9, pp. 1900-1906, Sept. 2014, doi:10.1109/TPAMI.2014.2299809