Transformational approach for alignment-free image matching applications

Komal Komal, Nandita Bhattacharjee, David Albrecht, Bala Srinivasan

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

    Abstract

    One of the fundamental steps in computer vision and image processing applications is the alignment of images before matching. A number of alignment methods based on features points, pixel information and transformation parameters have been proposed in the literature. All these methods either suffer from accurate detection of feature points or are computationally expensive that makes the entire matching process time consuming. The fast matching of images performed without alignment can benefit a number of image matching applications. In this paper, a transformational approach is proposed that identifies whether two images are similar or not without performing an alignment. The matching without alignment reduces the computation time of the entire matching process. The effectiveness of the proposed approach has been demonstrated on UMD logo dataset. The robustness of the proposed approach to additive noise shows that high accuracy can be achieved even when the noise is as high as 20%. Furthermore, the computation time is improved by decreasing the number of projections without affecting the matching accuracy. The experiments demonstrate that the proposed method is not only efficient but also highly accurate as compared to existing approaches.

    Original languageEnglish
    Title of host publicationThe 15th International Conference on Advances in Mobile Computing and Multimedia, December 04-06, 2017, Salzburg, Austria
    Subtitle of host publicationMoMM 2017 Proceedings
    EditorsEric Pardede, Pari Delir Haghighi, Ivan Luiz Salvadori, Matthias Steinbauer, Ismail Khalil, Gabriele Anderst-Kotsis
    Place of PublicationNew York NY USA
    PublisherAssociation for Computing Machinery (ACM)
    Pages49-57
    Number of pages9
    ISBN (Print)9781450353007
    DOIs
    Publication statusPublished - 4 Dec 2017
    EventInternational Conference on Advances in Mobile Computing and Multimedia 2017 - Salzburg, Austria
    Duration: 4 Dec 20176 Dec 2017
    Conference number: 15th
    http://www.iiwas.org/conferences/momm2017/

    Conference

    ConferenceInternational Conference on Advances in Mobile Computing and Multimedia 2017
    Abbreviated titleMoMM 2017
    CountryAustria
    CitySalzburg
    Period4/12/176/12/17
    Internet address

    Keywords

    • Alignment-free
    • Computation time
    • Logos
    • Precision-Recall
    • Radon transform

    Cite this

    Komal, K., Bhattacharjee, N., Albrecht, D., & Srinivasan, B. (2017). Transformational approach for alignment-free image matching applications. In E. Pardede, P. D. Haghighi, I. L. Salvadori, M. Steinbauer, I. Khalil, & G. Anderst-Kotsis (Eds.), The 15th International Conference on Advances in Mobile Computing and Multimedia, December 04-06, 2017, Salzburg, Austria: MoMM 2017 Proceedings (pp. 49-57). New York NY USA: Association for Computing Machinery (ACM). https://doi.org/10.1145/3151848.3151855
    Komal, Komal ; Bhattacharjee, Nandita ; Albrecht, David ; Srinivasan, Bala. / Transformational approach for alignment-free image matching applications. The 15th International Conference on Advances in Mobile Computing and Multimedia, December 04-06, 2017, Salzburg, Austria: MoMM 2017 Proceedings. editor / Eric Pardede ; Pari Delir Haghighi ; Ivan Luiz Salvadori ; Matthias Steinbauer ; Ismail Khalil ; Gabriele Anderst-Kotsis. New York NY USA : Association for Computing Machinery (ACM), 2017. pp. 49-57
    @inproceedings{81a9384e9a034de38a46212f02831a36,
    title = "Transformational approach for alignment-free image matching applications",
    abstract = "One of the fundamental steps in computer vision and image processing applications is the alignment of images before matching. A number of alignment methods based on features points, pixel information and transformation parameters have been proposed in the literature. All these methods either suffer from accurate detection of feature points or are computationally expensive that makes the entire matching process time consuming. The fast matching of images performed without alignment can benefit a number of image matching applications. In this paper, a transformational approach is proposed that identifies whether two images are similar or not without performing an alignment. The matching without alignment reduces the computation time of the entire matching process. The effectiveness of the proposed approach has been demonstrated on UMD logo dataset. The robustness of the proposed approach to additive noise shows that high accuracy can be achieved even when the noise is as high as 20{\%}. Furthermore, the computation time is improved by decreasing the number of projections without affecting the matching accuracy. The experiments demonstrate that the proposed method is not only efficient but also highly accurate as compared to existing approaches.",
    keywords = "Alignment-free, Computation time, Logos, Precision-Recall, Radon transform",
    author = "Komal Komal and Nandita Bhattacharjee and David Albrecht and Bala Srinivasan",
    year = "2017",
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    Komal, K, Bhattacharjee, N, Albrecht, D & Srinivasan, B 2017, Transformational approach for alignment-free image matching applications. in E Pardede, PD Haghighi, IL Salvadori, M Steinbauer, I Khalil & G Anderst-Kotsis (eds), The 15th International Conference on Advances in Mobile Computing and Multimedia, December 04-06, 2017, Salzburg, Austria: MoMM 2017 Proceedings. Association for Computing Machinery (ACM), New York NY USA, pp. 49-57, International Conference on Advances in Mobile Computing and Multimedia 2017, Salzburg, Austria, 4/12/17. https://doi.org/10.1145/3151848.3151855

    Transformational approach for alignment-free image matching applications. / Komal, Komal; Bhattacharjee, Nandita; Albrecht, David; Srinivasan, Bala.

    The 15th International Conference on Advances in Mobile Computing and Multimedia, December 04-06, 2017, Salzburg, Austria: MoMM 2017 Proceedings. ed. / Eric Pardede; Pari Delir Haghighi; Ivan Luiz Salvadori; Matthias Steinbauer; Ismail Khalil; Gabriele Anderst-Kotsis. New York NY USA : Association for Computing Machinery (ACM), 2017. p. 49-57.

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

    TY - GEN

    T1 - Transformational approach for alignment-free image matching applications

    AU - Komal, Komal

    AU - Bhattacharjee, Nandita

    AU - Albrecht, David

    AU - Srinivasan, Bala

    PY - 2017/12/4

    Y1 - 2017/12/4

    N2 - One of the fundamental steps in computer vision and image processing applications is the alignment of images before matching. A number of alignment methods based on features points, pixel information and transformation parameters have been proposed in the literature. All these methods either suffer from accurate detection of feature points or are computationally expensive that makes the entire matching process time consuming. The fast matching of images performed without alignment can benefit a number of image matching applications. In this paper, a transformational approach is proposed that identifies whether two images are similar or not without performing an alignment. The matching without alignment reduces the computation time of the entire matching process. The effectiveness of the proposed approach has been demonstrated on UMD logo dataset. The robustness of the proposed approach to additive noise shows that high accuracy can be achieved even when the noise is as high as 20%. Furthermore, the computation time is improved by decreasing the number of projections without affecting the matching accuracy. The experiments demonstrate that the proposed method is not only efficient but also highly accurate as compared to existing approaches.

    AB - One of the fundamental steps in computer vision and image processing applications is the alignment of images before matching. A number of alignment methods based on features points, pixel information and transformation parameters have been proposed in the literature. All these methods either suffer from accurate detection of feature points or are computationally expensive that makes the entire matching process time consuming. The fast matching of images performed without alignment can benefit a number of image matching applications. In this paper, a transformational approach is proposed that identifies whether two images are similar or not without performing an alignment. The matching without alignment reduces the computation time of the entire matching process. The effectiveness of the proposed approach has been demonstrated on UMD logo dataset. The robustness of the proposed approach to additive noise shows that high accuracy can be achieved even when the noise is as high as 20%. Furthermore, the computation time is improved by decreasing the number of projections without affecting the matching accuracy. The experiments demonstrate that the proposed method is not only efficient but also highly accurate as compared to existing approaches.

    KW - Alignment-free

    KW - Computation time

    KW - Logos

    KW - Precision-Recall

    KW - Radon transform

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    DO - 10.1145/3151848.3151855

    M3 - Conference Paper

    SN - 9781450353007

    SP - 49

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    BT - The 15th International Conference on Advances in Mobile Computing and Multimedia, December 04-06, 2017, Salzburg, Austria

    A2 - Pardede, Eric

    A2 - Haghighi, Pari Delir

    A2 - Salvadori, Ivan Luiz

    A2 - Steinbauer, Matthias

    A2 - Khalil, Ismail

    A2 - Anderst-Kotsis, Gabriele

    PB - Association for Computing Machinery (ACM)

    CY - New York NY USA

    ER -

    Komal K, Bhattacharjee N, Albrecht D, Srinivasan B. Transformational approach for alignment-free image matching applications. In Pardede E, Haghighi PD, Salvadori IL, Steinbauer M, Khalil I, Anderst-Kotsis G, editors, The 15th International Conference on Advances in Mobile Computing and Multimedia, December 04-06, 2017, Salzburg, Austria: MoMM 2017 Proceedings. New York NY USA: Association for Computing Machinery (ACM). 2017. p. 49-57 https://doi.org/10.1145/3151848.3151855