IEEE Trans Image Process - CSMMI: Class-Specific Maximization of Mutual Information for Action and Gesture Recognition.

Tópicos

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Resumo

In this paper, we propose a novel approach called CSMMI using a submodular method, which aims at learning a compact and discriminative dictionary for each class. Unlike traditional dictionary-based algorithms, which typically learn a shared dictionary for all of the classes, we unify the intra-class and inter-class mutual information (MI) into an single objective function to optimize class-specific dictionary. The objective function has two aims: (1) Maximizing the MI between dictionary items within a specific class (intrinsic structure); (2) Minimizing the MI between the dictionary items in a given class and those of the other classes (extrinsic structure). We significantly reduce the computational complexity of CSMMI by introducing an novel submodular method, which is one of the important contributions of this paper. Our paper also contributes a state-of-the-art endto- end system for action and gesture recognition incorporating CSMMI, with feature extraction, learning initial dictionary per each class by sparse coding, CSMMI via submodularity and classification based on reconstruction errors. We performed extensive experiments on synthetic data and eight benchmark datasets. Our experimental results show that CSMMI outperforms shared dictionary methods and that our end-to-end system is competitive with other state-of-the-art approaches.

Resumo Limpo

paper propos novel approach call csmmi use submodular method aim learn compact discrimin dictionari class unlik tradit dictionarybas algorithm typic learn share dictionari class unifi intraclass interclass mutual inform mi singl object function optim classspecif dictionari object function two aim maxim mi dictionari item within specif class intrins structur minim mi dictionari item given class class extrins structur signific reduc comput complex csmmi introduc novel submodular method one import contribut paper paper also contribut stateoftheart endto end system action gestur recognit incorpor csmmi featur extract learn initi dictionari per class spars code csmmi via submodular classif base reconstruct error perform extens experi synthet data eight benchmark dataset experiment result show csmmi outperform share dictionari method endtoend system competit stateoftheart approach

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