Speed up interactive image retrieval

Shen, Heng Tao, Jiang, Shouxu, Tan, Kian-Lee, Huang, Zi and Zhou, Xiaofang (2009) Speed up interactive image retrieval. The VLDB Journal, 18 1: 329-343. doi:10.1007/s00778-008-0101-6

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Author Shen, Heng Tao
Jiang, Shouxu
Tan, Kian-Lee
Huang, Zi
Zhou, Xiaofang
Title Speed up interactive image retrieval
Journal name The VLDB Journal   Check publisher's open access policy
ISSN 1066-8888
Publication date 2009-01-01
Year available 2008
Sub-type Article (original research)
DOI 10.1007/s00778-008-0101-6
Open Access Status Not Open Access
Volume 18
Issue 1
Start page 329
End page 343
Total pages 15
Place of publication Heidelberg, Germany
Publisher Springer
Language eng
Subject 0806 Information Systems
890205 Information Processing Services (incl. Data Entry and Capture)
Abstract In multimedia retrieval, a query is typically interactively refined towards the “optimal” answers by exploiting user feedback. However, in existing work, in each iteration, the refined query is re-evaluated. This is not only inefficient but fails to exploit the answers that may be common between iterations. Furthermore, it may also take too many iterations to get the “optimal” answers. In this paper, we introduce a new approach called OptRFS (optimizing relevance feedback search by query prediction) for iterative relevance feedback search. OptRFS aims to take users to view the “optimal” results as fast as possible. It optimizes relevance feedback search by both shortening the searching time during each iteration and reducing the number of iterations. OptRFS predicts the potential candidates for the next iteration and maintains this small set for efficient sequential scan. By doing so, repeated candidate accesses (i.e., random accesses) can be saved, hence reducing the searching time for the next iteration. In addition, efficient scan on the overlap before the next search starts also tightens the search space with smaller pruning radius. As a step forward, OptRFS also predicts the “optimal” query, which corresponds to “optimal” answers, based on the early executed iterations’ queries. By doing so, some intermediate iterations can be saved, hence reducing the total number of iterations. By taking the correlations among the early executed iterations into consideration, OptRFS investigates linear regression, exponential smoothing and linear exponential smoothing to predict the next refined query so as to decide the overlap of candidates between two consecutive iterations. Considering the special features of relevance feedback, OptRFS further introduces adaptive linear exponential smoothing to self-adjust the parameters for more accurate prediction. We implemented OptRFS and our experimental study on real life data sets show that it can reduce the total cost of relevance feedback search significantly. Some interesting features of relevance feedback search are also discovered and discussed.
Keyword Image retrieval
Relevance feedback
Query processing
Indexing
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: ERA 2012 Admin Only
School of Information Technology and Electrical Engineering Publications
 
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Created: Thu, 03 Sep 2009, 19:01:19 EST by Mr Andrew Martlew on behalf of School of Information Technol and Elec Engineering