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Title of Journal: Multimed Tools Appl

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Abbravation: Multimedia Tools and Applications

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Springer US

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DOI

10.1007/s00464-010-1528-6

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1573-7721

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A Learning to Rank framework applied to textimage

Authors: David Buffoni Sabrina Tollari Patrick Gallinari
Publish Date: 2011/05/25
Volume: 60, Issue: 1, Pages: 161-180
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Abstract

We present a framework based on a Learning to Rank setting for a textimage retrieval task In Information Retrieval the goal is to compute the similarity between a document and an user query In the context of textimage retrieval where several similarities exist human intervention is often needed to decide on the way to combine them On the other hand with the Learning to Rank approach the combination of the similarities is done automatically Learning to Rank is a paradigm where the learnt objective function is able to produce a ranked list of images when a user query is given These score functions are generally a combination of similarities between a document and a query In the past Learning to Rank algorithms were successfully applied to text retrieval where they outperformed baselines such as BM25 or TFIDF This inspired us to apply our stateoftheart algorithm called OWPC Usunier et al 2009 to the textimage retrieval task At this time no benchmarks are available therefore we present a framework for building one The empirical validation of this algorithm is done on the dataset constructed through comparison of typical textimage retrieval similarities In both cases visual only and text and visual our algorithm performs better than a simple baseline

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