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Large Scale Sparse Optimization for Object Detection in High Resolution Images

Abstract : In this work, we address the problem of detecting objects in images by expressing the image as convolutions between activation matrices and dictionary atoms. The activation matrices are estimated through sparse optimization and correspond to the position of the objects. In particular, we propose an efficient algorithm based on an active set strategy that is easily scalable and can be computed in parallel. We apply it to a toy image and a satellite image where the aim is to detect all the boats in a harbor. These results show the benefit of using nonconvex penalties, such as the log-sum penalty, over the convex l1 penalty.
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https://hal.inria.fr/hal-01066235
Contributor : Aurélie Boisbunon <>
Submitted on : Friday, September 19, 2014 - 2:24:15 PM
Last modification on : Thursday, March 25, 2021 - 11:46:03 AM

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  • HAL Id : hal-01066235, version 1

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Aurélie Boisbunon, Rémi Flamary, Alain Rakotomamonjy, Alain Giros, Josiane Zerubia. Large Scale Sparse Optimization for Object Detection in High Resolution Images. MLSP - 24th IEEE Workshop on Machine Learning for Signal Processing, Sep 2014, Reims, France. ⟨hal-01066235⟩

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