Analysis of the self projected matching pursuit algorithm

Rebollo-Neira, Laura; Rozložńık, Miroslav; Sasmal, Pradip

doi:10.1016/j.jfranklin.2020.06.006

Cited by 5 publications

(4 citation statements)

References 40 publications

(61 reference statements)

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“…The above described implementation of HBW-OMP2D is very effective in terms of speed, but demanding in terms of memory (the partial outputs corresponding to all the blocks in the partition need to be stored at every iteration). An alternative implementation, termed HBW Self Projected Matching Pursuit (HBW-SPMP) [ 29 , 45 ], would enable the application of the identical strategy to much larger images than the ones considered in this work .…”

Section: Approximations By Atomic Decompositionmentioning

confidence: 99%

Enhancing sparse representation of color images by cross channel transformation

Rebollo-Neira

Aurelien

2023

PLoS ONE

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Transformations for enhancing sparsity in the approximation of color images by 2D atomic decomposition are discussed. The sparsity is firstly considered with respect to the most significant coefficients in the wavelet decomposition of the color image. The discrete cosine transform is singled out as an effective 3 point transformation for this purpose. The enhanced feature is further exploited by approximating the transformed arrays using an effective greedy strategy with a separable highly redundant dictionary. The relevance of the achieved sparsity is illustrated by a simple encoding procedure. On typical test images the compression at high quality recovery is shown to significantly improve upon JPEG and WebP formats.

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Section: Approximations By Atomic Decompositionmentioning

confidence: 99%

Enhancing sparse representation of color images by cross channel transformation

Rebollo-Neira

Aurelien

2023

PLoS ONE

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“…a pedagogical proof can be found on [18]). However, the method is not stepwise optimal because it does not yield an orthogonal projection at each step.…”

Section: From Mp To Ompmentioning

confidence: 99%

Sparsity based morphological identification of heartbeats

Rebollo-Neira¹,

Battikh²,

Watt³

2023

Preprint

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Background: The electrocardiogram (ECG) is one of the most common primary tests to evaluate the health of the heart. Reliable automatic interpretation of ECG records is crucial to the goal of improving public health. It can enable a safe inexpensive monitoring. This work presents a new methodology for morphological identification of heartbeats, which is placed outside the usual machine learning framework.Method: The proposal considers the sparsity of the representation of a heartbeat as a parameter for morphological identification. The approach involves greedy algorithms for selecting elements from redundant dictionaries, which should be previously learnt from examples of the classes to be identified. Using different metrics of sparsity, the dictionary rendering the smallest sparsity value, for the equivalent approximation quality of a new heartbeat, classifies the morphology of that beat. This study focuses on a procedure of learning the dictionaries for representing heartbeats and compares several metrics of sparsity for morphological identification on the basis of those metrics.Results: The suitability of the method is illustrated by binary differentiation of Normal and Ventricular heartbeats in the MIT-BIH Arrhythmia data set. In general classification 99.7% of the Normal beats and 97.6% of the Ventricular beats in the testing sets are correctly identified. In interpatient assessment 91.8% of the Normal beats and 91.0% of Ventricular beats are correctly identified. Even more important than these scores is the fact that they are produced on the bases of a single parameter.Conclusions: The numerical tests, designed to emphasise the interpretability and reliability of the approach, demonstrate the potential of the method to contribute towards the development of a well grounded expert system for classification of heartbeats in ECG records.

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“…The above described implementation of HBW-OMP2D is very effective in terms of speed, but demanding in terms of memory (the partial outputs corresponding to all the blocks in the partition need to be stored at every iteration). An alternative implementation, termed HBW Self Projected Matching Pursuit (HBW-SPMP) [32,50], would enable the application of the identical strategy to much larger images than the ones considered in this work.…”

Section: Approximations By Atomic Decompositionmentioning

confidence: 99%

Enhancing sparse representation of color images by cross channel transformation

Rebollo-Neira¹,

Aurelien²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Transformations for enhancing sparsity in the approximation of color images by 2D atomic decomposition are discussed. The sparsity is firstly considered with respect to the most significant coefficients in the wavelet decomposition of the color image. The discrete cosine transform is singled out as an effective transformation for this purpose. The enhanced feature is further exploited by approximating the transformed arrays using an effective greedy strategy with a separable highly redundant dictionary. The relevance of the achieved sparsity is illustrated by a simple encoding procedure. On a set of typical test images the compression at high quality recovery is shown to significantly improve upon JPEG and WebP formats. The results are competitive with those produced by the JPEG2000 standard.

show abstract

Analysis of the self projected matching pursuit algorithm

Cited by 5 publications

References 40 publications

Enhancing sparse representation of color images by cross channel transformation

Enhancing sparse representation of color images by cross channel transformation

Sparsity based morphological identification of heartbeats

Enhancing sparse representation of color images by cross channel transformation

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