Cascaded Active Learning for Object Retrieval using Multiscale Coarse to Fine Analysis - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2011

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

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.
Fichier non déposé

Dates et versions

hal-01126007 , version 1 (06-03-2015)

Identifiants

  • HAL Id : hal-01126007 , version 1

Citer

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⟩
30 Consultations
0 Téléchargements

Partager

Gmail Facebook X LinkedIn More