A string matching approach for visual retrieval and classification

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A string matching approach for visual retrieval and classification

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International Multimedia Conference archive
Proceeding of the 1st ACM international conference on Multimedia information retrieval table of contents

Vancouver, British Columbia, Canada

SESSION: Image retrieval 1 table of contents

Pages 52-58

Year of Publication: 2008

ISBN:978-1-60558-312-9

Authors

Mei-Chen Yeh	University of California, Santa Barbara, Santa Barbara, CA, USA
Kwang-Ting Cheng	University of California, Santa Barbara, Santa Barbara, CA, USA

Sponsors

SIGMULTIMEDIA: ACM Special Interest Group on Multimedia
ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

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ABSTRACT

We present an approach to measuring similarities between visual data based on approximate string matching. In this approach, an image is represented by an ordered list of feature descriptors. We show the extraction of local features sequences from two types of 2-D signals - scene and shape images. The similarity of these two images is then measured by 1) solving a correspondence problem between two ordered sets of features and 2) calculating similarities between matched features and dissimilarities between unmatched features. Our experimental study shows that such a globally ordered and locally unordered representation is more discriminative than a bag-of-features representation and the similarity measure based on string matching is effective. We illustrate the application of the proposed approach to scene classification and shape retrieval, and demonstrate superior performance to existing solutions.

REFERENCES

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