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Pattern Retrieval in Large Image Databases Using Multiscale Coarse-to-Fine Cascaded Active Learning

Blanchart, Pierre and Ferecatu, Marin and Cui, Shiyong and Datcu, Mihai (2014) Pattern Retrieval in Large Image Databases Using Multiscale Coarse-to-Fine Cascaded Active Learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7 (4), pp. 1127-1141. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/JSTARS.2014.2302333 ISSN 1939-1404

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Official URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6736057


Pattern retrieval is a fundamental challenge in machine learning but is often subject to the problem of gathering enough labeled examples of the target pattern, and also to the computational complexity inherent to the training and the evaluation of complex classifier functions on large databases. In this paper, we propose a hierarchical top-down processing scheme for pattern retrieval in high-volume high-resolution optical satellite image repositories. We learn via a multistage active learning process a cascade of classifiers working each at a certain scale on a patch-based representation of images. At each stage of the hierarchy, we seek to eliminate large parts of images considered as nonrelevant, the purpose being to set the focus at the finest scales on more promising and as spatially limited as possible areas. Our scheme is based on the fact that by reducing the size of the analysis window (i.e., the size of the patch), we better capture the properties of the targeted object. The cascaded hierarchy is introduced to compensate for the extra computational burden incurred by diminishing the size of the patch, which causes an explosion of the number of patches to process. Unlike most other retrieval methods, which require large training sets and costly offline training, we propose a cascaded active learning strategy to build a classifier at each level of the hierarchy, and we provide a new Multiple Instance Learning algorithm to propagate automatically the training examples from one level of the hierarchy to the other. Two study cases are performed for validation. The first is a test on a database of 61-cm resolution QuickBird panchromatic images and the second is an example of temporal pattern retrieval from a database of Synthetic Aperture Radar (SAR) image time series. These tests show that our method achieves a reduction in the number of computations of two orders of magnitude, while keeping the same accuracy level as recent state-of-the-art methods.

Item URL in elib:https://elib.dlr.de/93683/
Document Type:Article
Title:Pattern Retrieval in Large Image Databases Using Multiscale Coarse-to-Fine Cascaded Active Learning
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Blanchart, PierreTelecom Paris Tech, Paris, FranceUNSPECIFIED
Ferecatu, MarinConservatoire National des Arts et Metiers, FranceUNSPECIFIED
Date:April 2014
Journal or Publication Title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1109/JSTARS.2014.2302333
Page Range:pp. 1127-1141
Chanussot, Jocelynjocelyn.chanussot@gipsa-lab.grenoble-inp.fr
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Active learning, coarse-to-fine testing, multiple instance learning (MIL), pattern retrieval, support vector machines (SVMs)
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited On:17 Dec 2014 09:23
Last Modified:08 Mar 2018 18:31

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