IEEE Trans Image Process - Scene-oriented hierarchical classification of blurry and noisy images.

Tópicos

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Resumo

A system for scene-oriented hierarchical classification of blurry and noisy images is proposed. It attempts to simulate important features of the human visual perception. The underlying approach is based on three strategies: extraction of essential signatures captured from a global context, simulating the global pathway; highlight detection based on local conspicuous features of the reconstructed image, simulating the local pathway; and hierarchical classification of extracted features using probabilistic techniques. The techniques involved in hierarchical classification use input from both the local and global pathways. Visual context is exploited by a combination of Gabor filtering with the principal component analysis. In parallel, a pseudo-restoration process is applied together with an affine invariant approach to improve the accuracy in the detection of local conspicuous features. Subsequently, the local conspicuous features and the global essential signature are combined and clustered by a Monte Carlo approach. Finally, clustered features are fed to a self-organizing tree algorithm to generate the final hierarchical classification results. Selected representative results of a comprehensive experimental evaluation validate the proposed system.

Resumo Limpo

system sceneori hierarch classif blurri noisi imag propos attempt simul import featur human visual percept under approach base three strategi extract essenti signatur captur global context simul global pathway highlight detect base local conspicu featur reconstruct imag simul local pathway hierarch classif extract featur use probabilist techniqu techniqu involv hierarch classif use input local global pathway visual context exploit combin gabor filter princip compon analysi parallel pseudorestor process appli togeth affin invari approach improv accuraci detect local conspicu featur subsequ local conspicu featur global essenti signatur combin cluster mont carlo approach final cluster featur fed selforgan tree algorithm generat final hierarch classif result select repres result comprehens experiment evalu valid propos system

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