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Towards relevance and saliency ranking of image tags

Published: 29 October 2012 Publication History

Abstract

Social image tag ranking has emerged as an important research topic recently due to its potential application on web image search. This paper presents an adaptive all-season tag ranking algorithm which can handle the images with and without distinct object(s) using different tag ranking strategies. Firstly, based on saliency map derived from the visual attention model, a linear SVM is trained to pre-classify an image as attentive or non-attentive category by using the gray histogram descriptor on the corresponding saliency map. Then, an image with distinct object is processed by an attention-driven tag saliency ranking algorithm emphasizing distinct object. On the other hand, an image without distinct object is processed by the tag relevance ranking algorithm via the sparse representation based neighbor-voting strategy. Such adaptive ranking strategy can be regarded as taking full advantage of existing tag ranking paradigms. Experiments conducted on well-known image data sets demonstrate the effectiveness and efficiency of the proposed framework.

References

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H. Fu, Z. Chi, and D. Feng. Combined retrieval strategies for images with and without distinct objects. In ACIVS, 2010.
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X. Li and et al. Learning social tag relevance by neighbor voting. IEEE Trans. on Multimedia., 20(11):1254--1259, 2009.
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Cited By

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  • (2019)Stacked Autoencoder Based Weak Supervision for Social Image UnderstandingIEEE Access10.1109/ACCESS.2019.28989917(21777-21786)Online publication date: 2019
  • (2016)Socializing the Semantic GapACM Computing Surveys10.1145/290615249:1(1-39)Online publication date: 6-Jun-2016
  • (2014)Query recommendation in the information domain of childrenJournal of the Association for Information Science and Technology10.1002/asi.2305565:7(1368-1384)Online publication date: 1-Jul-2014
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  1. Towards relevance and saliency ranking of image tags

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    cover image ACM Conferences
    MM '12: Proceedings of the 20th ACM international conference on Multimedia
    October 2012
    1584 pages
    ISBN:9781450310895
    DOI:10.1145/2393347
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 29 October 2012

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    Author Tags

    1. adaptive tag ranking
    2. multiple-instance learning
    3. sparse representation
    4. visual attention model

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    MM '12
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    MM '12: ACM Multimedia Conference
    October 29 - November 2, 2012
    Nara, Japan

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    Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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    Cited By

    View all
    • (2019)Stacked Autoencoder Based Weak Supervision for Social Image UnderstandingIEEE Access10.1109/ACCESS.2019.28989917(21777-21786)Online publication date: 2019
    • (2016)Socializing the Semantic GapACM Computing Surveys10.1145/290615249:1(1-39)Online publication date: 6-Jun-2016
    • (2014)Query recommendation in the information domain of childrenJournal of the Association for Information Science and Technology10.1002/asi.2305565:7(1368-1384)Online publication date: 1-Jul-2014
    • (2013)Adaptive all-season image tag ranking by saliency-driven image pre-classificationJournal of Visual Communication and Image Representation10.1016/j.jvcir.2013.06.01824:7(1031-1039)Online publication date: 1-Oct-2013

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