2D Shape Classification Using Multifractional Brownian Motion

Bicego, Manuele; Trudda, Alessandro

doi:10.1007/978-3-540-89689-0_94

Cited by 15 publications

(11 citation statements)

References 27 publications

(35 reference statements)

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“…Although we do not have, at this moment, a formal justification for this fact, it may be due to the following behavior of the Jensen-Tsallis kernels. For q < 1, the maximizer of [2], [4], [5], [7], [14], [15], [16], [17]. Although the experimental procedure is not the same in all those references, the results suggest that the proposed method performs better than the others.…”

Section: Resultsmentioning

confidence: 95%

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2D Shape Recognition Using Information Theoretic Kernels

Bicego

Martins²,

Murino

et al. 2010

2010 20th International Conference on Pattern Recognition

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Section: Resultsmentioning

confidence: 95%

“…1. This constitutes a challenging classification task, which has been studied by several authors [1], [5], [15].…”

Section: Resultsmentioning

confidence: 99%

2D Shape Recognition Using Information Theoretic Kernels

Bicego

Martins²,

Murino

et al. 2010

2010 20th International Conference on Pattern Recognition

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“…1. This constitutes a challenging classification task, which has been recently used as a benchmark by several authors [3,6,8,18,19,21,22].…”

Section: Experimental Evaluationmentioning

confidence: 99%

Information Theoretical Kernels for Generative Embeddings Based on Hidden Markov Models

Martins

Bicego

Murino

et al. 2010

Lecture Notes in Computer Science

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Abstract. Many approaches to learning classifiers for structured objects (e.g., shapes) use generative models in a Bayesian framework. However, state-of-the-art classifiers for vectorial data (e.g., support vector machines) are learned discriminatively. A generative embedding is a mapping from the object space into a fixed dimensional feature space, induced by a generative model which is usually learned from data. The fixed dimensionality of these feature spaces permits the use of state of the art discriminative machines based on vectorial representations, thus bringing together the best of the discriminative and generative paradigms.Using a generative embedding involves two steps: (i) defining and learning the generative model used to build the embedding; (ii) discriminatively learning a (maybe kernel) classifier on the adopted feature space. The literature on generative embeddings is essentially focused on step (i), usually adopting some standard off-the-shelf tool (e.g., an SVM with a linear or RBF kernel) for step (ii). In this paper, we follow a different route, by combining several Hidden Markov Models-based generative embeddings (including the classical Fisher score) with the recently proposed non-extensive information theoretic kernels. We test this methodology on a 2D shape recognition task, showing that the proposed method is competitive with the state-of-art.

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“…In [12] the authors characterize the contour of each object using the multifractional Brownian motion (mBm), using Horst coefficients to derive a fixed length vector, which characterizes each shape. After that a 1-NN classifiers (with the Euclidean and the Minkowsky distance) is used.…”

Section: Hidden Markov Modelsmentioning

confidence: 99%

A New Generative Feature Set Based on Entropy Distance for Discriminative Classification

Perina

Cristani

Castellani

et al. 2009

Image Analysis and Processing – ICIAP 2009

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Abstract. Score functions induced by generative models extract fixeddimensions feature vectors from different-length data observations by subsuming the process of data generation, projecting them in highly informative spaces called score spaces. In this way, standard discriminative classifiers such as support vector machines, or logistic regressors are proved to achieve higher performances than a solely generative or discriminative approach. In this paper, we present a novel score space that capture the generative process encoding it in an entropic feature vector. In this way, both uncertainty in the generative model learning step and "local" compliance of data observations with respect to the generative process can be represented. The proposed score space is presented for hidden Markov models and mixture of gaussian and is experimentally validated on standard benchmark datasets; moreover it can be applied to any generative model. Results show how it achieves compelling classification accuracies.

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2D Shape Classification Using Multifractional Brownian Motion

Cited by 15 publications

References 27 publications

2D Shape Recognition Using Information Theoretic Kernels

2D Shape Recognition Using Information Theoretic Kernels

Information Theoretical Kernels for Generative Embeddings Based on Hidden Markov Models

A New Generative Feature Set Based on Entropy Distance for Discriminative Classification

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