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Semisupervised online learning of hierarchical structures for visual object classification

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Abstract

One of the main challenges in hierarchical object classification is the derivation of the correct hierarchical structure. The classic way around the problem is assuming prior knowledge about the hierarchical structure itself. Two major drawbacks result from the former assumption. Firstly it has been shown that the hierarchies tend to reduce the differences between adjacent nodes. It has been observed that this trait of hierarchical models results in a less accurate classification. Secondly the mere assumption of prior knowledge about the form of the hierarchy requires an extra amount of information about the dataset that in many real world scenarios may not be available. In this work we address the mentioned problems by introducing online learning of hierarchical models. Our models start from a crude guess of the hierarchy and proceed to figure out the detailed version progressively. We show the merits of the proposed work via extensive simulations and experiments on a real objects database.

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Acknowledgements

The completion of this research was made possible thanks to the Natural Sciences and Engineering Research Council of Canada (NSERC). The authors would like to thank the anonymous referees and the associate editor for their helpful comments. The complete source code of this work is available upon request.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada

    Ali Shojaee Bakhtiari

  2. Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada

    Nizar Bouguila

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  1. Ali Shojaee Bakhtiari
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  2. Nizar Bouguila
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Correspondence to Nizar Bouguila.

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Bakhtiari, A.S., Bouguila, N. Semisupervised online learning of hierarchical structures for visual object classification. Multimed Tools Appl 74, 1805–1822 (2015). https://doi.org/10.1007/s11042-013-1719-y

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