IEEE Trans Image Process - Maximum Margin Correlation Filter: a new approach for localization and classification.

Tópicos

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Resumo

Support vector machine (SVM) classifiers are popular in many computer vision tasks. In most of them, the SVM classifier assumes that the object to be classified is centered in the query image, which might not always be valid, e.g., when locating and classifying a particular class of vehicles in a large scene. In this paper, we introduce a new classifier called Maximum Margin Correlation Filter (MMCF), which, while exhibiting the good generalization capabilities of SVM classifiers, is also capable of localizing objects of interest, thereby avoiding the need for image centering as is usually required in SVM classifiers. In other words, MMCF can simultaneously localize and classify objects of interest. We test the efficacy of the proposed classifier on three different tasks: vehicle recognition, eye localization, and face classification. We demonstrate that MMCF outperforms SVM classifiers as well as well known correlation filters.

Resumo Limpo

support vector machin svm classifi popular mani comput vision task svm classifi assum object classifi center queri imag might alway valid eg locat classifi particular class vehicl larg scene paper introduc new classifi call maximum margin correl filter mmcf exhibit good general capabl svm classifi also capabl local object interest therebi avoid need imag center usual requir svm classifi word mmcf can simultan local classifi object interest test efficaci propos classifi three differ task vehicl recognit eye local face classif demonstr mmcf outperform svm classifi well well known correl filter

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