Abstract
Given some kind of input data, the aim of learning is to build models that are able to represent the input data and generalize them for the recognition of previously unseen data. The input data is typically encoded in a training set that contains images or image sequences for different classes. Many different training datasets are publicly available. The caltech-256 dataset [83] is a challenging set of 256 object categories containing a total of 30607 images (see Fig. 4.1(a)). It was collected by choosing a set of object categories and downloading appropriate examples from the internet with a minimum number of 80 images in each category.
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© 2015 Springer International Publishing Switzerland
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Spehr, J. (2015). Learning of Hierarchical Models. In: On Hierarchical Models for Visual Recognition and Learning of Objects, Scenes, and Activities. Studies in Systems, Decision and Control, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-319-11325-8_4
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DOI: https://doi.org/10.1007/978-3-319-11325-8_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11324-1
Online ISBN: 978-3-319-11325-8
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