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10.1109/CVPR.2013.213guideproceedingsArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
Article

Lp-Norm IDF for Large Scale Image Search

Published: 23 June 2013 Publication History

Abstract

The Inverse Document Frequency (IDF) is prevalently utilized in the Bag-of-Words based image search. The basic idea is to assign less weight to terms with high frequency, and vice versa. However, the estimation of visual word frequency is coarse and heuristic. Therefore, the effectiveness of the conventional IDF routine is marginal, and far from optimal. To tackle this problem, this paper introduces a novel IDF expression by the use of Lp-norm pooling technique. Carefully designed, the proposed IDF takes into account the term frequency, document frequency, the complexity of images, as well as the codebook information. Optimizing the IDF function towards optimal balancing between TF and pIDF weights yields the so-called Lp-norm IDF (pIDF). We show that the conventional IDF is a special case of our generalized version, and two novel IDFs, i.e. the average IDF and the max IDF, can also be derived from our formula. Further, by counting for the term-frequency in each image, the proposed Lp-norm IDF helps to alleviate the visual word burstiness phenomenon. Our method is evaluated through extensive experiments on three benchmark datasets (Oxford 5K, Paris 6K and Flickr 1M). We report a performance improvement of as large as 27.1% over the baseline approach. Moreover, since the Lp-norm IDF is computed offline, no extra computation or memory cost is introduced to the system at all.

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  • (2019)Non-negative locality-constrained vocabulary tree for finger vein image retrievalFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-017-6583-x13:2(318-332)Online publication date: 17-May-2019
  • (2019)Multiple complementary inverted indexing based on multiple metricsMultimedia Tools and Applications10.1007/s11042-018-6439-x78:6(7727-7747)Online publication date: 17-May-2019
  • (2018)Deep hashing with top similarity preserving for image retrievalMultimedia Tools and Applications10.1007/s11042-017-5596-777:18(24121-24141)Online publication date: 1-Sep-2018
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Published In

cover image Guide Proceedings
CVPR '13: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition
June 2013
3752 pages
ISBN:9780769549897

Publisher

IEEE Computer Society

United States

Publication History

Published: 23 June 2013

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Cited By

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  • (2019)Non-negative locality-constrained vocabulary tree for finger vein image retrievalFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-017-6583-x13:2(318-332)Online publication date: 17-May-2019
  • (2019)Multiple complementary inverted indexing based on multiple metricsMultimedia Tools and Applications10.1007/s11042-018-6439-x78:6(7727-7747)Online publication date: 17-May-2019
  • (2018)Deep hashing with top similarity preserving for image retrievalMultimedia Tools and Applications10.1007/s11042-017-5596-777:18(24121-24141)Online publication date: 1-Sep-2018
  • (2018)Is visual saliency useful for content-based image retrieval?Multimedia Tools and Applications10.1007/s11042-017-5001-677:11(13983-14006)Online publication date: 1-Jun-2018
  • (2017)Robust ImageGraphIEEE Transactions on Image Processing10.1109/TIP.2017.266024426:7(3128-3141)Online publication date: 1-Jul-2017
  • (2017)Separable vocabulary and feature fusion for image retrieval based on sparse representationNeurocomputing10.1016/j.neucom.2016.08.106236:C(14-22)Online publication date: 2-May-2017
  • (2017)MapReduce-based clustering for near-duplicate image identificationMultimedia Tools and Applications10.1007/s11042-016-4060-476:22(23291-23307)Online publication date: 1-Nov-2017
  • (2016)3D object retrieval with multimodal viewsProceedings of the Eurographics 2016 Workshop on 3D Object Retrieval10.5555/3056462.3056480(99-106)Online publication date: 8-May-2016
  • (2016)Adaptively Weighted Graph Fusion for Image RetrievalProceedings of the International Conference on Internet Multimedia Computing and Service10.1145/3007669.3007688(18-21)Online publication date: 19-Aug-2016
  • (2016)SERVENeurocomputing10.1016/j.neucom.2016.04.047207:C(202-212)Online publication date: 26-Sep-2016
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