Titlebook: Computer Vision -- ECCV 2006; 9th European Confere Ale? Leonardis,Horst Bischof,Axel Pinz Conference proceedings 2006 Springer-Verlag Berli

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書目名稱Computer Vision -- ECCV 2006影響因子(影響力)




書目名稱Computer Vision -- ECCV 2006影響因子(影響力)學(xué)科排名




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書目名稱Computer Vision -- ECCV 2006網(wǎng)絡(luò)公開度學(xué)科排名




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書目名稱Computer Vision -- ECCV 2006被引頻次學(xué)科排名




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書目名稱Computer Vision -- ECCV 2006年度引用學(xué)科排名




書目名稱Computer Vision -- ECCV 2006讀者反饋




書目名稱Computer Vision -- ECCV 2006讀者反饋學(xué)科排名

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Weakly Supervised Learning of Part-Based Spatial Models for Visual Object Recognitionon about class membership (and not object location or configuration). This method learns both a model of local part appearance and a model of the spatial relations between those parts. In contrast, other work using such a weakly supervised learning paradigm has not considered the problem of simultan

frozen-shoulder

Hyperfeatures – Multilevel Local Coding for Visual Recognitionto local occlusions and to geometric and photometric variations, but they are not able to exploit spatial co-occurrence statistics at scales larger than their local input patches. We present a new multilevel visual representation, ‘hyperfeatures’, that is designed to remedy this. The starting point

inclusive

Riemannian Manifold Learning for Nonlinear Dimensionality Reductionce. We propose an efficient algorithm called Riemannian manifold learning (RML). A Riemannian manifold can be constructed in the form of a simplicial complex, and thus its intrinsic dimension can be reliably estimated. Then the NLDR problem is solved by constructing Riemannian normal coordinates (RN

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Conditional Infomax Learning: An Integrated Framework for Feature Extraction and Fusiontion structure and present a novel perspective revealing the two key factors in information utilization: class-relevance and redundancy. We derive a new information decomposition model where a novel concept called class-relevant redundancy is introduced. Subsequently a new algorithm called Condition

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Riemannian Manifold Learning for Nonlinear Dimensionality Reductioncomplex, and thus its intrinsic dimension can be reliably estimated. Then the NLDR problem is solved by constructing Riemannian normal coordinates (RNC). Experimental results demonstrate that our algorithm can learn the data’s intrinsic geometric structure, yielding uniformly distributed and well organized low-dimensional embedding data.

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