NDLI: Texture modelling with generic translation- and contrast/offset-invariant $2^{nd}

Content Provider	IEEE Xplore Digital Library
Author	Ni Liu Gimel'farb, G. Delmas, P.
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci., Univ. of Auckland, Auckland, New Zealand (Ni Liu; Gimel'farb, G.; Delmas, P.)
Abstract	A conventional framework for learning generic translation-invariant $2^{nd}-order$ Markov-Gibbs random field (MGRF) models of spatially homogeneous textures is extended onto higher-order ones, which are also invariant to arbitrary perceptive (contrast-offset) signal deviations. Given a training image, the framework estimates both the geometry and strengths (potentials) of multiple conditional signal dependencies, called interactions. The potentials are approximated analytically, and characteristic interactions are selected by analysing an empirical distribution of energies (sums of the potentials) for a large number of candidate $3^{rd}-$ and $4^{th}-order$ interactions. Descriptive abilities of the learned generic translation- and contrast/offset-invariant $2^{nd}-4^{th}-order$ MGRFs are tested on 50 classes of textures from the Brodatz and OUTEX databases in application to semi-supervised texture recognition. Comparing to our previous work [11], contributions of this paper are two-fold. (i) To analyse the classification performance trend, the MGRF models have been extended up to the $4^{th}$ order. (ii) In order to select characteristic interactions, a heuristic iterative application of unimodal thresholding to the energy distribution in [11] is replaced by estimating dominant modes of this distribution. The latter is approximated with a Gaussian mixture, using the Expectation-Maximization (EM) algorithm, the number of the mixture components having been determined by the Akaike Information Criterion (AIC). The goal interactions are selected by either unimodal thresholding or finding an intersection between the mixture components related to the lowest and the second-lowest energy modes.
Sponsorship	IEEE New Zealand Central Sect.
Starting Page	370
Ending Page	375
File Size	1241489
Page Count	6
File Format	PDF
ISSN	21512191
e-ISBN	9781479908837
DOI	10.1109/IVCNZ.2013.6727043
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-11-27
Publisher Place	New Zealand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Maximum likelihood estimation Databases Computational modeling Image edge detection Lattices Joints
Content Type	Text
Resource Type	Article

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