IEEE Trans Image Process - Efficient HIK SVM learning for image classification.

Tópicos

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Resumo

Histograms are used in almost every aspect of image processing and computer vision, from visual descriptors to image representations. Histogram intersection kernel (HIK) and support vector machine (SVM) classifiers are shown to be very effective in dealing with histograms. This paper presents contributions concerning HIK SVM for image classification. First, we propose intersection coordinate descent (ICD), a deterministic and scalable HIK SVM solver. ICD is much faster than, and has similar accuracies to, general purpose SVM solvers and other fast HIK SVM training methods. We also extend ICD to the efficient training of a broader family of kernels. Second, we show an important empirical observation that ICD is not sensitive to the C parameter in SVM, and we provide some theoretical analyses to explain this observation. ICD achieves high accuracies in many problems, using its default parameters. This is an attractive property for practitioners, because many image processing tasks are too large to choose SVM parameters using cross-validation.

Resumo Limpo

histogram use almost everi aspect imag process comput vision visual descriptor imag represent histogram intersect kernel hik support vector machin svm classifi shown effect deal histogram paper present contribut concern hik svm imag classif first propos intersect coordin descent icd determinist scalabl hik svm solver icd much faster similar accuraci general purpos svm solver fast hik svm train method also extend icd effici train broader famili kernel second show import empir observ icd sensit c paramet svm provid theoret analys explain observ icd achiev high accuraci mani problem use default paramet attract properti practition mani imag process task larg choos svm paramet use crossvalid

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