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Object and Action Classification with Latent Window Parameters

Tijdschriftbijdrage - Tijdschriftartikel

In this paper we propose a generic frame- work to incorporate unobserved auxiliary information for classifying objects and actions. This framework al- lows us to automatically select a bounding box and its quadrants from which best to extract features. These spatial subdivisions are learnt as latent variables. The paper is an extended version of our earlier work [2], complemented with additional ideas, experiments and analysis. We approach the classification problem in a discrim- inative setting, as learning a max-margin classifier that infers the class label along with the latent variables. Through this paper we make the following contribu- tions: a) we provide a method for incorporating latent variables into object and action classification; b) these variables determine the relative focus on foreground vs. background information that is taken account of; c) we design an objective function to more effectively learn in unbalanced data sets; d) we learn a better classifier by iterative expansion of the latent parameter space. We demonstrate the performance of our approach through experimental evaluation on a number of standard object and action recognition data sets.
Tijdschrift: International Journal of Computer Vision
ISSN: 0920-5691
Issue: 3
Volume: 106
Pagina's: 237 - 251
Jaar van publicatie:2014
BOF-keylabel:ja
IOF-keylabel:ja
BOF-publication weight:10
CSS-citation score:1
Auteurs:International
Authors from:Private, Higher Education
Toegankelijkheid:Open