[BFD11d] Cascaded Active Learning for Object Retrieval using Multiscale Coarse to Fine Analysis

Conférence Internationale avec comité de lecture : IEEE Conference on Image Processing (ICIP 2011), September 2011, Bruxelles, (DOI: 10.1109/ICIP.2011.6116251)

Mots clés: statistical learning, image database, Earth Observation repositories, active learning, object detection, relevance feedback

Résumé: 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.

Equipe: vertigo
Collaboration: DLR


@inproceedings {
title="{Cascaded Active Learning for Object Retrieval using Multiscale Coarse to Fine Analysis}",
author=" P. Blanchart and M. Ferecatu and M. Datcu ",
booktitle="{IEEE Conference on Image Processing (ICIP 2011)}",
address="Bruxelles, ",