Skip to Main content Skip to Navigation
Conference papers

Active Boosting for interactive object retrieval.

Abstract : This paper presents a new algorithm based on boost- ing for interactive object retrieval in images. Recent works propose ”online boosting” algorithms where weak classifier sets are iteratively trained from data. These algorithms are proposed for visual tracking in videos, and are not well adapted to ”online boosting” for interactive retrieval. We propose in this paper to iteratively build weak classifiers from images, labeled as positive by the user during a retrieval session. A novel active learning strategy for the selection of im- ages for user annotation is also proposed. This strategy is used to enhance the strong classifier resulting from ”boosting” process, but also to build new weak classi- fiers. Experiments have been carried out on a generalist database in order to compare the proposed method to a SVM based reference approach.
Document type :
Conference papers
Complete list of metadata

Cited literature [15 references]  Display  Hide  Download
Contributor : Philippe-Henri Gosselin Connect in order to contact the contributor
Submitted on : Wednesday, September 22, 2010 - 6:46:32 PM
Last modification on : Friday, August 5, 2022 - 2:45:59 PM
Long-term archiving on: : Thursday, December 23, 2010 - 3:16:25 AM


Publisher files allowed on an open archive


  • HAL Id : hal-00520294, version 1



Alexis Lechervy, Philippe-Henri Gosselin, Frédéric Precioso. Active Boosting for interactive object retrieval.. International Conference on Pattern Recognition, Aug 2010, Turkey. pp.1. ⟨hal-00520294⟩



Record views


Files downloads