Retour de pertinence | EPFL Graph Search

Le retour de pertinence est un domaine particulier de la recherche d'information qui a pour but de prendre en compte le point de vue des utilisateurs sur les documents présentés. Dans les années 1970, on trouve aussi les travaux de Rocchio sur le retour explicite de pertinence. L'idée essentielle était alors qu'il était possible d'affiner la requête initiale des utilisateurs au fur et à mesure que ceux-ci fournissaient des jugements de pertinence sur des documents consultés. Le système classique de recherche d'information était donc doté d'une boucle de retour qui permettait au système de reformuler la requête posée et donc d'avancer dans la recherche. Références Catégorie:Informatique théorique Catégorie:Recherche d'information Catégorie:Traitement automatique du langage naturel

Adaptive Relevance Feedback for Large-scale Image Retrieval

Nicolae Suditu

Our research addresses the need for an efficient, effective, and interactive access to large-scale image collections. Image retrieval needs are evolving beyond the capabilities of the traditional indexing based on manual annotation, and the most desirable characteristic of any image retrieval system is to be able to deal with automatically-extracted visual indexing features, while providing an intuitive and simple interaction with users. In this thesis, we investigate an innovative query-free retrieval approach that was proposed by Ferecatu and Geman. Starting from an heuristic sampling of the collection, this approach does not require any explicit query, neither keywords nor image-examples. It relies solely on an iterative relevance feedback mechanism driven by the user's subjective judgments of image similarities. At each iteration, the system displays a small set of images and the user is asked to choose the image that best matches in her opinion what she is searching for. The system updates an internal state based on automatically-extracted indexing features, and it displays a new set of images accordingly. The idea is that the system converges towards what the user is searching for, and iteratively it displays more and more relevant images. Our contributions are related to four complementary aspects of the iterative relevance feedback mechanism. First, we formalize a large-scale approach based on a hierarchical tree-like organization of the images computed off-line. Second, we propose a versatile modulation of the exploration/exploitation trade-off based on the consistency of the system internal states between successive iterations. Third, we elaborate a long-term optimization of the similarity metric based on the user searching session logs accumulated off-line. Forth, we propose a dynamic short-term adaptation of the similarity metric based on the relevance feedback events accumulated on-the-fly at each iteration. Furthermore, we round up our research by integrating all our contributions together into one comprehensive retrieval system. Experimental validation was carried out by implementing a web-application which includes all our contributions. This software is distributed to the public under the AGPL Version 3 open-source license. We carried out plenty of user-based evaluation campaigns, and we analyzed systematically all our contributions. We show empirically that each of them improves significantly the retrieval performance of the original framework. Moreover, we show that they are complementary to each other, and their overall integration is consistently beneficial. We foresee that our contributions, along with our open-source web-application, will motivate further investigations and facilitate further experiments. We hope that our research brings the iterative relevance feedback mechanism one step closer to commercial applications.

EPFL2013

Adaptive relevance feedback for large-scale image retrieval

François Fleuret, Nicolae Suditu

Content-based image retrieval aims at substituting traditional indexing based on manual annotation by using automatically-extracted visual indexing features. Novel techniques are needed however to efficiently deal with the semantic gap (i.e. the partial match between the low-level features and the visual content). Here, we investigate a query-free retrieval approach first proposed by Ferecatu and Geman. This approach relies solely on an iterative relevance feedback mechanism that drives a heuristic sampling of the collection, and aims to take explicitly into account the semantic gap. Our contributions are related to three complementary aspects. First, we formalize a large-scale approach based on a hierarchical tree-like organization of the images computed off-line. Second, we propose a versatile modulation of the exploration/exploitation trade-off based on the consistency of the system internal states between successive iterations. Third, we elaborate a long-term optimization of the similarity metric based on the user searching session logs accumulated off-line. We implemented a web-application that integrates all our contributions, and distribute it under the AGPL Version 3 free software license. We organized user-based evaluation campaigns using ImageNet dataset, and show empirically that our contributions significantly improve the retrieval performance of the original framework, that they are complementary to each other, and that their overall integration is consistently beneficial.

2016

Adaptive Relevance Feedback for Large-scale Image Retrieval

Nicolae Suditu

EPFL2013