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[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
BibTeX
| @inproceedings { | |||
| BFD11d, | |||
| 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)}", | |
| year | = | 2011, | |
| month | = | "September", | |
| address | = | "Bruxelles, ", | |
| doi | = | "10.1109/ICIP.2011.6116251", | |
| } | |||
