Probabilistic Models for Combining Diverse Knowledge Sources in Multimedia Retrieval (2006)
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| Venue: | In Ph.D Thesis |
| Citations: | 18 - 2 self |
BibTeX
@TECHREPORT{Yan06probabilisticmodels,
author = {Rong Yan},
title = {Probabilistic Models for Combining Diverse Knowledge Sources in Multimedia Retrieval},
institution = {In Ph.D Thesis},
year = {2006}
}
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Abstract
In recent years, the multimedia retrieval community is gradually shifting its emphasis from analyzing one media source at a time to exploring the opportunities of combining diverse knowledge sources from correlated media types and context. This thesis presents a conditional probabilistic retrieval model as a principled framework to combine diverse knowledge sources. An efficient rank-based learning approach has been developed to explicitly model the ranking relations in the learning process. Under this retrieval framework, we overview and develop a number of state-of-the-art approaches for extracting ranking features from multimedia knowledge sources. To incorporate query information in the combination model, this thesis develops a number of query analysis models that can automatically discover mixing structure of the query space based on previous retrieval results. To adapt the combination function on a per query basis, this thesis also presents a probabilistic local context analysis(pLCA) model to automatically leverage additional retrieval sources to improve initial retrieval outputs. All the proposed approaches are evaluated on multimedia retrieval tasks with large-scale video collections as well as meta-search tasks with large-scale text collections. 1
