In this paper we present a hybrid generative-discriminative approach for image categorization in real-world images, based on Latent Dirichlet Allocation and SVM classifiers. We use SVMs with non-linear kernels on different visual features in a multiple kernel combination framework. A major contribution of our work is also the introduction of a novel dataset, called MICC-Flickr101, based on the popular Caltech101 and collected from Flickr. We demonstrate the effectiveness and efficiency of our method testing it on both datasets, and we evaluate the impact of combining image features and tags for object recognition.
Combining Generative and Discriminative Models for Classifying Social Images from 101 Object Categories / L., Ballan; M., Bertini; A., Del Bimbo; A. M., Serain; Serra, Giuseppe; B. F., Zaccone. - STAMPA. - (2012), pp. 1731-1734. (Intervento presentato al convegno 21st International Conference on Pattern Recognition, ICPR 2012 tenutosi a Tsukuba, jpn nel 2012).
Combining Generative and Discriminative Models for Classifying Social Images from 101 Object Categories
SERRA, GIUSEPPE;
2012
Abstract
In this paper we present a hybrid generative-discriminative approach for image categorization in real-world images, based on Latent Dirichlet Allocation and SVM classifiers. We use SVMs with non-linear kernels on different visual features in a multiple kernel combination framework. A major contribution of our work is also the introduction of a novel dataset, called MICC-Flickr101, based on the popular Caltech101 and collected from Flickr. We demonstrate the effectiveness and efficiency of our method testing it on both datasets, and we evaluate the impact of combining image features and tags for object recognition.
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