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Matching pursuit-based shape representation and recognition using scale-space

Publications associées (33)

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We propose a novel sparse dictionary learning method for planar shapes in the sense of Kendall, namely configurations of landmarks in the plane considered up to similitudes. Our shape dictionary method provides a good trade-off between algorithmic simplici ...

SIAM PUBLICATIONS2020

A deep learning framework for matching of SAR and optical imagery

Devis Tuia, Sylvain Lobry, Lloyd Haydn Hughes

SAR and optical imagery provide highly complementary information about observed scenes. A combined use of these two modalities is thus desirable in many data fusion scenarios. However, any data fusion task requires measurements to be accurately aligned. Wh ...

2020

On Matching Pursuit and Coordinate Descent

Martin Jaggi, Sebastian Urban Stich, Sai Praneeth Reddy Karimireddy, Anant Raj

Two popular examples of first-order optimization methods over linear spaces are coordinate descent and matching pursuit algorithms, with their randomized variants. While the former targets the optimization by moving along coordinates, the latter considers ...

2018

A new mathematical representation of demand using choice-based optimization method

Michel Bierlaire, Shadi Sharif Azadeh, Meritxell Pacheco Paneque

Discrete choice models are the state-of-the-art for the mathematical modeling of demand. Based on the concept of random utility, they are able to predict the choice behavior of individuals. However, these models are highly non linear and non convex in the ...

2016

In this work a new method for automatic image classification is proposed. It relies on a compact representation of images using sets of sparse binary features. This work first evaluates the Fast Retina Keypoint binary descriptor and proposes imp ...

2013

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This paper introduces a novel algorithm for sparse approximation in redundant dictionaries called the M-term pursuit (MTP). This algorithm decomposes a signal into a linear combination of atoms that are selected in order to represent the main signal compon ...

Institute of Electrical and Electronics Engineers2012

Are Sparse Representations Really Relevant for Image Classification?

Vincent Lepetit, Roberto Rigamonti, Matthew Brown

Recent years have seen an increasing interest in sparse representations for image classiﬁcation and object recognition, probably motivated by evidence from the analysis of the primate visual cortex. It is still unclear, however, whether or not sparsity hel ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Is Sparsity Really Relevant for Image Classification?

Vincent Lepetit, Roberto Rigamonti, Matthew Brown

Recent years have seen an increasing interest in sparseness constraints for image classiﬁcation and object recognition, probably motivated by the evidence of sparse representations internal in the primate visual cortex. It is still unclear, however, whethe ...

2010

Demand has emerged for next generation visual technologies that go beyond conventional 2D imaging. Such technologies should capture and communicate all perceptually relevant three-dimensional information about an environment to a distant observer, providin ...

EPFL2009

Over the past few decades we have been experiencing a data explosion; massive amounts of data are increasingly collected and multimedia databases, such as YouTube and Flickr, are rapidly expanding. At the same time rapid technological advancements in mobil ...

EPFL2009