---
_id: '2012'
abstract:
- lang: eng
  text: "The rapid growth of optical imaging technologies increased the access and
    collection of data, which boosts the demand of data and knowledge discovery. This
    is a fast growing topic in several industry and research areas. Nowadays, a large
    number of images and signals must be analysed in order to gain and learn proper
    knowledge. Detecting images with similar contents without specifying an image,
    recently attracts the researches in image processing domain. Motif discovery in
    image processing aims to tackle the problem of deriving structures or detecting
    regularities in image databases. Most of the motif discovery methods solve this
    problem by converting images into one dimensional time series in a pre-processing
    step and then applying a motif discovery on these one dimensional time series
    for image motifs detection. Nevertheless, this conversion might lead to information
    loss and also the problem of inability to discover shifted and multi-scale image
    motifs of different size. Contrary to other approaches, here, a method is proposed
    to find image motifs of different size in image data sets by employing images
    in original dimension (2D) without converting them to one dimensional time series.\r\nThe
    proposed approach consists of three steps: Mapping or transformation, feature
    extraction and measuring similarities. First, images are inspected by the Complex
    Quad Tree Wavelet Packet transform, which provides broad frequency analysis of
    an image in various scales. Next, statistical features are extracted from the
    wavelet coefficients. Finally, image motifs are detected by measuring the similarity
    of the features applying various similarity measures. Here, the performance of
    six similarity measures are benchmarked in details. Moreover, the efficiency of
    the proposed method is demonstrated on a data set with images from diverse applications
    such as hand gesture, text recognition, leaf and plant identification, etc. Additionally,
    the robustness of this method is examined with the image data overlaying with
    distortions such as noise and blur."
author:
- first_name: Sahar
  full_name: Deppe, Sahar
  id: '52121'
  last_name: Deppe
- first_name: Volker
  full_name: Lohweg, Volker
  id: '1804'
  last_name: Lohweg
  orcid: 0000-0002-3325-7887
citation:
  ama: Deppe S, Lohweg V. Evaluation of Similarity Measures for Shift-Invariant Image
    Motif Discovery. <i>International Journal On Advances in Intelligent Systems</i>.
    2017;10(3/4):434-446.
  apa: Deppe, S., &#38; Lohweg, V. (2017). Evaluation of Similarity Measures for Shift-Invariant
    Image Motif Discovery. <i>International Journal On Advances in Intelligent Systems</i>,
    <i>10</i>(3/4), 434–446.
  bjps: <b>Deppe S and Lohweg V</b> (2017) Evaluation of Similarity Measures for Shift-Invariant
    Image Motif Discovery. <i>International Journal On Advances in Intelligent Systems</i>
    <b>10</b>, 434–446.
  chicago: 'Deppe, Sahar, and Volker Lohweg. “Evaluation of Similarity Measures for
    Shift-Invariant Image Motif Discovery.” <i>International Journal On Advances in
    Intelligent Systems</i> 10, no. 3/4 (2017): 434–46.'
  chicago-de: 'Deppe, Sahar und Volker Lohweg. 2017. Evaluation of Similarity Measures
    for Shift-Invariant Image Motif Discovery. <i>International Journal On Advances
    in Intelligent Systems</i> 10, Nr. 3/4: 434–446.'
  din1505-2-1: '<span style="font-variant:small-caps;">Deppe, Sahar</span> ; <span
    style="font-variant:small-caps;">Lohweg, Volker</span>: Evaluation of Similarity
    Measures for Shift-Invariant Image Motif Discovery. In: <i>International Journal
    On Advances in Intelligent Systems</i> Bd. 10.   [S.l.], IARIA Journals (2017),
    Nr. 3/4, S. 434–446'
  havard: S. Deppe, V. Lohweg, Evaluation of Similarity Measures for Shift-Invariant
    Image Motif Discovery, International Journal On Advances in Intelligent Systems.
    10 (2017) 434–446.
  ieee: S. Deppe and V. Lohweg, “Evaluation of Similarity Measures for Shift-Invariant
    Image Motif Discovery,” <i>International Journal On Advances in Intelligent Systems</i>,
    vol. 10, no. 3/4, pp. 434–446, 2017.
  mla: Deppe, Sahar, and Volker Lohweg. “Evaluation of Similarity Measures for Shift-Invariant
    Image Motif Discovery.” <i>International Journal On Advances in Intelligent Systems</i>,
    vol. 10, no. 3/4, IARIA Journals, 2017, pp. 434–46.
  short: S. Deppe, V. Lohweg, International Journal On Advances in Intelligent Systems
    10 (2017) 434–446.
  ufg: '<b>Deppe, Sahar/Lohweg, Volker (2017)</b>: Evaluation of Similarity Measures
    for Shift-Invariant Image Motif Discovery, in: <i>International Journal On Advances
    in Intelligent Systems</i> <i>10</i> (<i>3/4</i>), S. 434–446.'
  van: Deppe S, Lohweg V. Evaluation of Similarity Measures for Shift-Invariant Image
    Motif Discovery. International Journal On Advances in Intelligent Systems. 2017;10(3/4):434–46.
date_created: 2019-11-25T08:35:54Z
date_updated: 2023-03-15T13:49:38Z
department:
- _id: DEP5023
intvolume: '        10'
issue: 3/4
keyword:
- processing
- Wavelet transformation.
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: http://www.iariajournals.org/intelligent_systems/intsys_v10_n34_2017_paged.pdf
oa: '1'
page: 434 - 446
place: '  [S.l.]'
publication: International Journal On Advances in Intelligent Systems
publication_identifier:
  issn:
  - 1942-2679
publication_status: published
publisher: IARIA Journals
status: public
title: Evaluation of Similarity Measures for Shift-Invariant Image Motif Discovery
type: journal_article
user_id: '45673'
volume: 10
year: 2017
...
---
_id: '2022'
abstract:
- lang: eng
  text: 'Nowadays, the boost of optical imaging technologies results in more data
    with a faster rate are being collected. Consequently, data and knowledge discovery
    science has become an attractive and a fast growing topic in several industry
    and research area. Motif discovery in image processing aims to tackle the problem
    of deriving structures or detecting regularities in image databases. Most of the
    motif discovery methods first convert images into time series and then attempt
    to find motifs in such data. This might lead to information loss and also the
    problem of inability to detect shifted and multi-scale image motifs  of different
    size. Here, a method is proposed to find image motifs of different size in image
    datasets by applying images in original dimension without converting them to time
    series. Images are inspected by the Complex Quad Tree Wavelet Packet transform
    which provides broad frequency analysis of an image in various scales. Next, features
    are extracted from the wavelet coefficients. Finally, image motifs are detected
    by measuring the similarity of the features. The performance of the proposed method
    is demonstrated on a dataset with images from diverse applications, such as hand
    gesture, text recognition, leaf and plant identification, etc. '
author:
- first_name: Sahar
  full_name: Deppe, Sahar
  id: '52121'
  last_name: Deppe
- first_name: Volker
  full_name: Lohweg, Volker
  id: '1804'
  last_name: Lohweg
  orcid: 0000-0002-3325-7887
citation:
  ama: 'Deppe S, Lohweg V. Shift-Invariant Motif Discovery in Image Processing “Best
    Paper Award.” In: Leister W, ed. <i>PESARO 2017 The Seventh International Conference
    on Performance, Safety and Robustness in Complex Systems and Applications</i>.
    PESARO 2017; Venice, Italy  : The Seventh International Conference on Performance,
    Safety and Robustness in Complex Systems and Applications; Special track MAIS:
    Machine Learning Algorithms in Image and Signal Processing; 2017:27-32.'
  apa: 'Deppe, S., &#38; Lohweg, V. (2017). Shift-Invariant Motif Discovery in Image
    Processing “Best Paper Award.” In W. Leister (Ed.), <i>PESARO 2017 The Seventh
    International Conference on Performance, Safety and Robustness in Complex Systems
    and Applications</i> (pp. 27–32). PESARO 2017; Venice, Italy  : The Seventh International
    Conference on Performance, Safety and Robustness in Complex Systems and Applications;
    Special track MAIS: Machine Learning Algorithms in Image and Signal Processing.'
  bjps: '<b>Deppe S and Lohweg V</b> (2017) Shift-Invariant Motif Discovery in Image
    Processing ‘Best Paper Award’. In Leister W (ed.), <i>PESARO 2017 The Seventh
    International Conference on Performance, Safety and Robustness in Complex Systems
    and Applications</i>. PESARO 2017; Venice, Italy  : The Seventh International
    Conference on Performance, Safety and Robustness in Complex Systems and Applications;
    Special track MAIS: Machine Learning Algorithms in Image and Signal Processing,
    pp. 27–32.'
  chicago: 'Deppe, Sahar, and Volker Lohweg. “Shift-Invariant Motif Discovery in Image
    Processing ‘Best Paper Award.’” In <i>PESARO 2017 The Seventh International Conference
    on Performance, Safety and Robustness in Complex Systems and Applications</i>,
    edited by Wolfgang Leister, 27–32. PESARO 2017; Venice, Italy  : The Seventh International
    Conference on Performance, Safety and Robustness in Complex Systems and Applications;
    Special track MAIS: Machine Learning Algorithms in Image and Signal Processing,
    2017.'
  chicago-de: 'Deppe, Sahar und Volker Lohweg. 2017. Shift-Invariant Motif Discovery
    in Image Processing „Best Paper Award“. In: <i>PESARO 2017 The Seventh International
    Conference on Performance, Safety and Robustness in Complex Systems and Applications</i>,
    hg. von Wolfgang Leister, 27–32. PESARO 2017; Venice, Italy  : The Seventh International
    Conference on Performance, Safety and Robustness in Complex Systems and Applications;
    Special track MAIS: Machine Learning Algorithms in Image and Signal Processing.'
  din1505-2-1: '<span style="font-variant:small-caps;">Deppe, Sahar</span> ; <span
    style="font-variant:small-caps;">Lohweg, Volker</span>: Shift-Invariant Motif
    Discovery in Image Processing „Best Paper Award“. In: <span style="font-variant:small-caps;">Leister,
    W.</span> (Hrsg.): <i>PESARO 2017 The Seventh International Conference on Performance,
    Safety and Robustness in Complex Systems and Applications</i>. PESARO 2017; Venice,
    Italy   : The Seventh International Conference on Performance, Safety and Robustness
    in Complex Systems and Applications; Special track MAIS: Machine Learning Algorithms
    in Image and Signal Processing, 2017, S. 27–32'
  havard: 'S. Deppe, V. Lohweg, Shift-Invariant Motif Discovery in Image Processing
    “Best Paper Award,” in: W. Leister (Ed.), PESARO 2017 The Seventh International
    Conference on Performance, Safety and Robustness in Complex Systems and Applications,
    The Seventh International Conference on Performance, Safety and Robustness in
    Complex Systems and Applications; Special track MAIS: Machine Learning Algorithms
    in Image and Signal Processing, PESARO 2017; Venice, Italy  , 2017: pp. 27–32.'
  ieee: S. Deppe and V. Lohweg, “Shift-Invariant Motif Discovery in Image Processing
    ‘Best Paper Award,’” in <i>PESARO 2017 The Seventh International Conference on
    Performance, Safety and Robustness in Complex Systems and Applications</i>, Venice,
    Italy , 2017, pp. 27–32.
  mla: 'Deppe, Sahar, and Volker Lohweg. “Shift-Invariant Motif Discovery in Image
    Processing ‘Best Paper Award.’” <i>PESARO 2017 The Seventh International Conference
    on Performance, Safety and Robustness in Complex Systems and Applications</i>,
    edited by Wolfgang Leister, The Seventh International Conference on Performance,
    Safety and Robustness in Complex Systems and Applications; Special track MAIS:
    Machine Learning Algorithms in Image and Signal Processing, 2017, pp. 27–32.'
  short: 'S. Deppe, V. Lohweg, in: W. Leister (Ed.), PESARO 2017 The Seventh International
    Conference on Performance, Safety and Robustness in Complex Systems and Applications,
    The Seventh International Conference on Performance, Safety and Robustness in
    Complex Systems and Applications; Special track MAIS: Machine Learning Algorithms
    in Image and Signal Processing, PESARO 2017; Venice, Italy  , 2017, pp. 27–32.'
  ufg: '<b>Deppe, Sahar/Lohweg, Volker (2017)</b>: Shift-Invariant Motif Discovery
    in Image Processing „Best Paper Award“, in: Wolfgang Leister (Hg.): <i>PESARO
    2017 The Seventh International Conference on Performance, Safety and Robustness
    in Complex Systems and Applications</i>, PESARO 2017; Venice, Italy  , S. 27–32.'
  van: 'Deppe S, Lohweg V. Shift-Invariant Motif Discovery in Image Processing “Best
    Paper Award.” In: Leister W, editor. PESARO 2017 The Seventh International Conference
    on Performance, Safety and Robustness in Complex Systems and Applications. PESARO
    2017; Venice, Italy  : The Seventh International Conference on Performance, Safety
    and Robustness in Complex Systems and Applications; Special track MAIS: Machine
    Learning Algorithms in Image and Signal Processing; 2017. p. 27–32.'
conference:
  end_date: 2017-04-27
  location: 'Venice, Italy '
  name: 7. International Conference on Performance (PESARO 2017)
  start_date: 2017-04-23
date_created: 2019-11-25T09:06:50Z
date_updated: 2023-03-15T13:49:38Z
department:
- _id: DEP5023
editor:
- first_name: Wolfgang
  full_name: Leister, Wolfgang
  last_name: Leister
keyword:
- Motif discovery
- Image processing
- Wavelet transformation
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: http://www.thinkmind.org/index.php?view=instance&instance=PESARO+2017
oa: '1'
page: 27-32
place: 'PESARO 2017; Venice, Italy  '
publication: PESARO 2017 The Seventh International Conference on Performance, Safety
  and Robustness in Complex Systems and Applications
publication_identifier:
  eisbn:
  - 978-1-61208-549-4
  eissn:
  - 2308-3700
publication_status: published
publisher: 'The Seventh International Conference on Performance, Safety and Robustness
  in Complex Systems and Applications; Special track MAIS: Machine Learning Algorithms
  in Image and Signal Processing'
status: public
title: Shift-Invariant Motif Discovery in Image Processing 'Best Paper Award'
type: conference
user_id: '15514'
year: 2017
...