IEEE Trans Image Process - Decomposition and extraction: a new framework for visual classification.

Tópicos

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Resumo

In this paper, we present a novel framework for visual classification based on hierarchical image decomposition and hybrid midlevel feature extraction. Unlike most midlevel feature learning methods, which focus on the process of coding or pooling, we emphasize that the mechanism of image composition also strongly influences the feature extraction. To effectively explore the image content for the feature extraction, we model a multiplicity feature representation mechanism through meaningful hierarchical image decomposition followed by a fusion step. In particularly, we first propose a new hierarchical image decomposition approach in which each image is decomposed into a series of hierarchical semantical components, i.e, the structure and texture images. Then, different feature extraction schemes can be adopted to match the decomposed structure and texture processes in a dissociative manner. Here, two schemes are explored to produce property related feature representations. One is based on a single-stage network over hand-crafted features and the other is based on a multistage network, which can learn features from raw pixels automatically. Finally, those multiple midlevel features are incorporated by solving a multiple kernel learning task. Extensive experiments are conducted on several challenging data sets for visual classification, and experimental results demonstrate the effectiveness of the proposed method.

Resumo Limpo

paper present novel framework visual classif base hierarch imag decomposit hybrid midlevel featur extract unlik midlevel featur learn method focus process code pool emphas mechan imag composit also strong influenc featur extract effect explor imag content featur extract model multipl featur represent mechan meaning hierarch imag decomposit follow fusion step particular first propos new hierarch imag decomposit approach imag decompos seri hierarch semant compon ie structur textur imag differ featur extract scheme can adopt match decompos structur textur process dissoci manner two scheme explor produc properti relat featur represent one base singlestag network handcraft featur base multistag network can learn featur raw pixel automat final multipl midlevel featur incorpor solv multipl kernel learn task extens experi conduct sever challeng data set visual classif experiment result demonstr effect propos method

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