Superpixel Matching Based Image Retrieval

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Author(s)

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Detail(s)

Original languageEnglish
Title of host publicationICVIP 2017 : Proceedings of the International Conference on Video and Image Processing
PublisherACM
Pages156-160
ISBN (Print)9781450353830
Publication statusPublished - 27 Dec 2017

Conference

Title2017 International Conference on Video and Image Processing (ICVIP 2017)
LocationNanyang Executive Centre
PlaceSingapore
CitySingapore
Period27 - 29 December 2017

Abstract

Local features of images have been widely used in image retrieval, however, the cost is so heavy. To address this issue, a superpixel-based approach for image retrieval is proposed. We first extract the image structure that preserves the main information and removes the redundant information from the image by smoothing and oversegment a smoothed image into a certain number of superpixels. We then extract the positive candidate superpixels by combining superpixels with local descriptors. Finally, we compute the similarity of two images by analyzing two sets of positive candidate superpixels. Experiments on dataset PQ7 demonstrate the performance of the proposed approach.

Research Area(s)

  • Image retrieval, Key-point, Smoothing, Superpixel

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

Superpixel Matching Based Image Retrieval. / He, Zhixiang; Sun, Xiaoli; Li, Chenhui; Baciu, George; Li, Yushi.

ICVIP 2017 : Proceedings of the International Conference on Video and Image Processing. ACM, 2017. p. 156-160.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review