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# Robust image representation for classification, retrieval and object discovery

1 LinkMedia - Creating and exploiting explicit links between multimedia fragments
Inria Rennes – Bretagne Atlantique , IRISA-D6 - MEDIA ET INTERACTIONS
Abstract : Neural network representations proved to be relevant for many computer vision tasks such as image classification, object detection, segmentation or instance-level image retrieval. A network is trained for one particular task and requires a large number of labeled data. We propose in this thesis solutions to extract the most information with the least supervision. First focusing on the classification task, we examine the active learning process in the context of deep learning and show that combining it to semi-supervised and unsupervised techniques boost greatly results. We then investigate the image retrieval task, and in particular we exploit the spatial localization information available for free'' in CNN feature maps. We first propose to represent an image by a collection of affine local features detected within activation maps, which are memory-efficient and robust enough to perform spatial matching. Then again extracting information from feature maps, we discover objects of interest in images of a dataset and gather their representations in a nearest neighbor graph. Using the centrality measure on the graph, we are able to construct a saliency map per image which focuses on the repeating objects and allows us to compute a global representation excluding clutter and background.
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https://hal.inria.fr/tel-03082952
Contributor : Oriane Siméoni <>
Submitted on : Friday, December 18, 2020 - 6:28:25 PM
Last modification on : Friday, January 8, 2021 - 3:40:22 AM

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thesis_O_SIMEONI.pdf
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• HAL Id : tel-03082952, version 1

### Citation

Oriane Siméoni. Robust image representation for classification, retrieval and object discovery. Computer Science [cs]. Université rennes1, 2020. English. ⟨tel-03082952⟩

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