Confidence interval approach to feature re-weighting

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Relevance feedback is commonly incorporated into content-based image retrieval systems with the objective of improving retrieval accuracy via user feedback. One effective method for improving retrieval performance is to perform feature re-weighting based on the obtained feedback. Previous approaches to feature re-weighting via relevance feedback assume the feature data for images can be represented in fixed-length vectors. However, many approaches are invalidated with the recent development of features that cannot be represented in fixed-length vectors. In addition, previous approaches use only the information from the set of images returned in the latest query result for feature re-weighting. In this paper, we propose a feature re-weighting approach that places no restriction on the representation of feature data and utilizes the aggregate set of images returned over the iterations of retrieval to obtain feature re-weighting information. The approach analyzes the feature distances calculated between the query image and the resulting set of images to approximate the feature distances for the entire set of images in the database. Two-sided confidence intervals are used with the distances to obtain the information for feature re-weighting. There is no restriction on how the distances are calculated for each feature. This provides freedom for how the feature representations are structured. The experimental results show the effectiveness of the proposed approach and in comparisons with other work, it is shown that our approach outperforms previous work.
Publisher
SPRINGER
Issue Date
2008-12
Language
English
Article Type
Article
Keywords

IMAGE RETRIEVAL-SYSTEM; DESCRIPTORS

Citation

MULTIMEDIA TOOLS AND APPLICATIONS, v.40, pp.385 - 407

ISSN
1380-7501
DOI
10.1007/s11042-008-0212-5
URI
http://hdl.handle.net/10203/92455
Appears in Collection
CS-Journal Papers(저널논문)
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