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Neural Computing and Applications

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08-04-2016 | IBPRIA 2015

Random clustering ferns for multimodal object recognition

Authors: M. Villamizar, A. Garrell, A. Sanfeliu, F. Moreno-Noguer

Published in: Neural Computing and Applications | Issue 9/2017

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Abstract

We propose an efficient and robust method for the recognition of objects exhibiting multiple intra-class modes, where each one is associated with a particular object appearance. The proposed method, called random clustering ferns, combines synergically a single and real-time classifier, based on the boosted assembling of extremely randomized trees (ferns), with an unsupervised and probabilistic approach in order to recognize efficiently object instances in images and discover simultaneously the most prominent appearance modes of the object through tree-structured visual words. In particular, we use boosted random ferns and probabilistic latent semantic analysis to obtain a discriminative and multimodal classifier that automatically clusters the response of its randomized trees in function of the visual object appearance. The proposed method is validated extensively in synthetic and real experiments, showing that the method is capable of detecting objects with diverse and complex appearance distributions in real-time performance.

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We use interchangeably the terms cluster and mode to refer to a dense part of the object appearance distribution.

The indicator function \({\mathbb {I}}(e)=1\) if e is true, and 0 otherwise.

The EER is the point in the precision-recall curve where precision = recall.

The score distribution (Gaussian function) is calculated using the confidences of the BRFs for all class samples.

The squared Hellinger distance for two distributions P and Q is defined as: \(H^2(P,Q) = 1 -\sqrt{k_1/k_2}\exp (-0.25k_3/k_2)\), with \(k_1 = 2 \sigma _P \sigma _Q\), \(k_2=\sigma _P^2 + \sigma _Q^2\), and \(k_3 =(\mu _P - \mu _Q)^2\).

However, it is possible to use human assistance during the learning to improve the visual skills of the classifier [40].

The code is available at http://www.iri.upc.edu/people/mvillami/code.html.

For this problem, only 300 visual words are activated out of 38,400 words, each one corresponding to a fern output.

Since the pLSA clustering is automatic, the confusion matrix is not necessarily diagonal. However, here the labels provided by pLSA have been sorted for display purposes.

The code for RCFs is available at http://www.iri.upc.edu/people/mvillami/code.html.

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Title: Random clustering ferns for multimodal object recognition
Authors: M. Villamizar
A. Garrell
A. Sanfeliu
F. Moreno-Noguer
Publication date: 08-04-2016
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 9/2017
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2284-x

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