Speed up interactive image retrieval

  • Authors:

  • Heng Tao Shen;Shouxu Jiang;Kian-Lee Tan;Zi Huang;Xiaofang Zhou

  • Affiliations:

  • School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia;Department of Computer Science, Harbin Institute of Technology, Harbin, China;Department of Computer Science, National University of Singapore, Singapore, Singapore;School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia;School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia

  • Venue:
  • The VLDB Journal — The International Journal on Very Large Data Bases
  • Year:
  • 2009

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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.