IEEE Trans Image Process - Texture classification by modeling joint distributions of local patterns with gaussian mixtures.

Tópicos

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Resumo

Texture classification generally requires the analysis of patterns in local pixel neighborhoods. Statistically, the underlying processes are comprehensively described by their joint probability density functions (jPDFs). Even for small neighborhoods, however, stable estimation of jPDFs by joint histograms (jHSTs) is often infeasible, since the number of entries in the jHST exceeds by far the number of pixels in a typical texture region. Moreover, evaluation of distance functions between jHSTs is often computationally prohibitive. Practically, the number of entries in a jHST is therefore reduced by considering only two-pixel patterns, leading to 2D-jHSTs known as cooccurrence matrices, or by quantization of the gray levels in local patterns to only two gray levels, yielding local binary patterns (LBPs). Both approaches result in a loss of information. We introduce here a framework for supervised texture classification which reduces or avoids this information loss. Local texture neighborhoods are first filtered by a filter bank. Without further quantization, the jPDF of the filter responses is then described parametrically by gaussian mixture models (GMMs). We show that the parameters of the GMMs can be reliably estimated from small image regions. Moreover, distances between the thus modelled jPDFs of different texture patterns can be computed efficiently in closed form from their model parameters. We furthermore extend this texture descriptor to achieve full invariance to rotation. We evaluate the framework for different filter banks on the Brodatz texture set. We first show that combining the LBP difference filters with the GMM-based density estimator outperforms the classical LBP approach and its codebook extensions. When replacing these-rather elementary-difference filters by the wavelet frame transform (WFT), the performance of the framework on all 111 Brodatz textures exceeds the one obtained more recently by spin image and RIFT descriptors by Lazebnik et al.

Resumo Limpo

textur classif general requir analysi pattern local pixel neighborhood statist under process comprehens describ joint probabl densiti function jpdfs even small neighborhood howev stabl estim jpdfs joint histogram jhsts often infeas sinc number entri jhst exceed far number pixel typic textur region moreov evalu distanc function jhsts often comput prohibit practic number entri jhst therefor reduc consid twopixel pattern lead djhsts known cooccurr matric quantiz gray level local pattern two gray level yield local binari pattern lbps approach result loss inform introduc framework supervis textur classif reduc avoid inform loss local textur neighborhood first filter filter bank without quantiz jpdf filter respons describ parametr gaussian mixtur model gmms show paramet gmms can reliabl estim small imag region moreov distanc thus model jpdfs differ textur pattern can comput effici close form model paramet furthermor extend textur descriptor achiev full invari rotat evalu framework differ filter bank brodatz textur set first show combin lbp differ filter gmmbase densiti estim outperform classic lbp approach codebook extens replac theserath elementarydiffer filter wavelet frame transform wft perform framework brodatz textur exceed one obtain recent spin imag rift descriptor lazebnik et al

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