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Cascaded Active Learning for Object Retrieval using Multiscale Coarse to Fine Analysis

Abstract : In this paper, we describe an active learning scheme which performs coarse to fine testing using a multiscale patch-based representation of images to retrieve objects in large satellite image repositories. The proposed hierarchical top-down approach reduces step by step the size of the analysis window, eliminating each time large parts of the images considered as non-relevant. Unlike most object detection methods which requires large training sets and costly offline training, we use an active learning strategy to build a classifier at each level of the hierarchy and we propose an algorithm to propagate automatically the training examples from one level to the other.
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https://hal.archives-ouvertes.fr/hal-01126007
Contributor : Laboratoire Cedric <>
Submitted on : Friday, March 6, 2015 - 11:36:21 AM
Last modification on : Thursday, February 6, 2020 - 2:16:06 PM

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  • HAL Id : hal-01126007, version 1

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Pierre Blanchart, Marin Ferecatu, Mihai Datcu. Cascaded Active Learning for Object Retrieval using Multiscale Coarse to Fine Analysis. IEEE Conference on Image Processing (ICIP 2011), Sep 2011, Bruxelles, France. ⟨hal-01126007⟩

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