IEEE Trans Image Process - Interactive image segmentation using Dirichlet process multiple-view learning.

Tópicos

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Resumo

Segmenting semantically meaningful whole objects from images is a challenging problem, and it becomes especially so without higher level common sense reasoning. In this paper, we present an interactive segmentation framework that integrates image appearance and boundary constraints in a principled way to address this problem. In particular, we assume that small sets of pixels, which are referred to as seed pixels, are labeled as the object and background. The seed pixels are used to estimate the labels of the unlabeled pixels using Dirichlet process multiple-view learning, which leverages 1) multiple-view learning that integrates appearance and boundary constraints and 2) Dirichlet process mixture-based nonlinear classification that simultaneously models image features and discriminates between the object and background classes. With the proposed learning and inference algorithms, our segmentation framework is experimentally shown to produce both quantitatively and qualitatively promising results on a standard dataset of images. In particular, our proposed framework is able to segment whole objects from images given insufficient seeds.

Resumo Limpo

segment semant meaning whole object imag challeng problem becom especi without higher level common sens reason paper present interact segment framework integr imag appear boundari constraint principl way address problem particular assum small set pixel refer seed pixel label object background seed pixel use estim label unlabel pixel use dirichlet process multipleview learn leverag multipleview learn integr appear boundari constraint dirichlet process mixturebas nonlinear classif simultan model imag featur discrimin object background class propos learn infer algorithm segment framework experiment shown produc quantit qualit promis result standard dataset imag particular propos framework abl segment whole object imag given insuffici seed

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