Abstract
In this paper, we present the motivation, process, results and analysis of results that we have worked on as part of our participation in the 2015 MediaEval Retrieving Diverse Social Images Task. This year, we adapted a recently-published technique for result diversification ("Relational Learning-to-Rank" [13]), borrowed from the world of standard document retrieval. As compared to the original work, our version makes certain changes to the ranking and comparison algorithm, and explores a variety of feature combinations specific to an image retrieval context. The key idea behind our technique was a greedy iterative approach to ranking search results, which attempted to balance relevance with redundancy by comparing candidate results to those already selected by the algorithm. Our approach worked tolerably well on many queries, but there is clearly room for improvement.
| Original language | English (US) |
|---|---|
| Journal | Unknown Journal |
| Volume | 1436 |
| State | Published - 2015 |
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ASJC Scopus subject areas
- Computer Science(all)
Cite this
Ohsu @ MediaEval 2015 : Adapting textual techniques to multimedia search. / Dudy, Shiran; Bedrick, Steven.
In: Unknown Journal, Vol. 1436, 2015.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Ohsu @ MediaEval 2015
T2 - Adapting textual techniques to multimedia search
AU - Dudy, Shiran
AU - Bedrick, Steven
PY - 2015
Y1 - 2015
N2 - In this paper, we present the motivation, process, results and analysis of results that we have worked on as part of our participation in the 2015 MediaEval Retrieving Diverse Social Images Task. This year, we adapted a recently-published technique for result diversification ("Relational Learning-to-Rank" [13]), borrowed from the world of standard document retrieval. As compared to the original work, our version makes certain changes to the ranking and comparison algorithm, and explores a variety of feature combinations specific to an image retrieval context. The key idea behind our technique was a greedy iterative approach to ranking search results, which attempted to balance relevance with redundancy by comparing candidate results to those already selected by the algorithm. Our approach worked tolerably well on many queries, but there is clearly room for improvement.
AB - In this paper, we present the motivation, process, results and analysis of results that we have worked on as part of our participation in the 2015 MediaEval Retrieving Diverse Social Images Task. This year, we adapted a recently-published technique for result diversification ("Relational Learning-to-Rank" [13]), borrowed from the world of standard document retrieval. As compared to the original work, our version makes certain changes to the ranking and comparison algorithm, and explores a variety of feature combinations specific to an image retrieval context. The key idea behind our technique was a greedy iterative approach to ranking search results, which attempted to balance relevance with redundancy by comparing candidate results to those already selected by the algorithm. Our approach worked tolerably well on many queries, but there is clearly room for improvement.
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M3 - Article
AN - SCOPUS:84989840245
VL - 1436
JO - Indian Journal of Rheumatology
JF - Indian Journal of Rheumatology
SN - 0973-3698
ER -