{"language":[{"iso":"eng"}],"main_file_link":[{"url":"http://www.iariajournals.org/intelligent_systems/intsys_v10_n34_2017_paged.pdf","open_access":"1"}],"oa":"1","volume":10,"type":"journal_article","_id":"2012","intvolume":"        10","year":2017,"author":[{"first_name":"Sahar","last_name":"Deppe","id":"52121","full_name":"Deppe, Sahar"},{"id":"1804","full_name":"Lohweg, Volker","first_name":"Volker","last_name":"Lohweg","orcid":"0000-0002-3325-7887"}],"keyword":["processing","Wavelet transformation."],"publication_status":"published","title":"Evaluation of Similarity Measures for Shift-Invariant Image Motif Discovery","date_created":"2019-11-25T08:35:54Z","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."}],"publisher":"IARIA Journals","department":[{"_id":"DEP5023"}],"user_id":"45673","citation":{"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.","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.","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.","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.","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.","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","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.","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.","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.","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.","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.","short":"S. Deppe, V. Lohweg, International Journal On Advances in Intelligent Systems 10 (2017) 434–446."},"page":"434 - 446","status":"public","date_updated":"2023-03-15T13:49:38Z","publication":"International Journal On Advances in Intelligent Systems","issue":"3/4","place":"  [S.l.]","publication_identifier":{"issn":["1942-2679"]}}