Abstract
The goal of this paper is to study the set of features that is suitable for describing animals in images, and for being able to categorize them in natural scenes. We propose multi-scale features based on Gaussian derivatives functions, that show interesting invariance properties. In order to build an efficient system, we will use classifiers based on the JointBoosting methodology, which will be compared with the well-known one-vs-all approach by using Support Vector Machines. Thirty five categories, containing animals, are selected from the challenging Caltech 101 object categories database to carry out the study.
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Marín-Jiménez, M.J., de la Blanca, N.P. (2006). Sharing Visual Features for Animal Categorization: An Empirical Study. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11867661_2
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DOI: https://doi.org/10.1007/11867661_2
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