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FARKLI BOYUTLARDA GÖRÜNTÜLERDE UYARLAMALI YEREL PENCERE İLE BENZERLİK ÖLÇÜMÜ

Year 2017, Volume: 5 Issue: 3, 609 - 614, 21.12.2017
https://doi.org/10.21923/jesd.328160

Abstract

İçerik tabanlı görüntü erişim yöntemleri, renk,
desen ve şekil bilgileri gibi farklı özelliklere ihtiyaç duymaktadır. Araştırmacılar,
görüntü histogramından elde edilen verileri de bu bağlamda kullanmaktadır.
Histogram bilgileri, yerel veya global olarak hesaplanır. Ancak, aynı içeriğe
sahip olsalar da, farklı en / boy oranlarına sahip görüntülerde yerel
yaklaşımlar kullanılamamakta ve tüm pikselleri işleyen yöntemler ile de her
zaman istenilen sonuca varılamamaktadır. Bu çalışmada, farklı boyutlarda iki
görüntüden, eşit sayıda pencere alınarak, görüntülerin benzerlik ölçümünde
kullanılan ve yerel histograma dayanan yeni bir yöntem geliştirilmiştir.
Geliştirilen yöntem, Weizmann tekli nesne görüntü bölütleme veritabanındaki 100
görüntü üzerinde test edilmiş ve yöntemin başarısı global histogram
yaklaşımlarıyla karşılaştırılmıştır.

References

  • Alpert, S., Galun, M., Brandt, A., & Basri, R., 2012. Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration. IEEE transactions on pattern analysis and machine intelligence, 34(2), 315-327.
  • Chitkara, V., 2001. Color-Based Image Retrieval Using Compact Binary Signatures, Tech. Report TR 01-08, University of Alberta Edmonton, Alberta, Canada.
  • Ioka, M., 1989. A Method of Defining the Similarity of Images on the Basis of Color Information, Tech. ReportRT-0030, IBM Tokyo Research Lab.
  • Niblack, C.W., Barber, R., Equitz, V., Flickner, M.D., Glasman, E.H., Petkovic, D., … & Taubin, G., 1993. The QBIC Project: Querying Images by Content Using Color, Texture and Shape, Proc. of IS & T/SPIE Int. Symp. on Electronic Imaging: Science & Technology, 1908: Storage and Retrieval for Image and Video Databases, 173-187.
  • Stricker, M., Dimai, A., 1997. Spectral Covariance and Fuzzy Regions for Image Indexing, Machine Vision Applications, 10(2), 66–73.
  • Stricker, M., Orengo, M., 1995. Similarity of Color Images, Proc. of the SPIE Conf., 2420, 381–392.
  • Swain, M.J., Ballard, D.H., 1991. Color Indexing, Int. J. Computer Vision, 7(1), 11–32.
  • Tanyeri, U., İncetaş, M.O., Aydemir, Z., 2017. Gürültülü Görüntü Üzerinde İnterpolasyon Etkisinin Filtre Yöntemleri ile İncelenmesi, 25. IEEE Sinyal İsleme ve İletişim Uygulamaları Kurultayı.
  • TinEye. (n.d.). Retrieved December 29, 2016, from http://www.tineye.com/faq#similar.
  • Vassilieva, N.S., 2009. Content-based Image Retrieval Methods. Programming and Computer Software, 35(3), 158-180.
  • Vassilieva, N., Novikov, B., 2005. Construction of Correspondences between Low-level Characteristics and Semantics of Static Images, Proc. of the 7th All-Russian Scientific Conf. “Electronic Libraries: Perspective Methods and Technologies, Electronic Collections”, RCDL’2005, Yaroslavl’, Russia.
  • Wang, Z., Lu, L., Bovik, A.C., 2004. Video Quality Assessment Based on Structural Distortion Measurement. Signal processing: Image communication, 19(2), 121-132.
  • What is the Algorithm Used by Google's Reverse Image Search (i.e. search by image)? (n.d.). Retrieved December 29, 2016, from https://www.quora.com/What-is-the-algorithm-used-by-Googles-reverse-image-search-i-e-search-by-image?redirected_qid=828413.
  • Who were They? (n.d.). Retrieved May 24, 2017, from https://www.myheritage.com.tr/
  • Xu, S., Li, C., Jiang, S., Liu, X.P., 2012. Similarity Measures for Content-Based Image Retrieval Based on Intuitionistic Fuzzy Set Theory. JCP, 7(7), 1733-1742.
  • Zhang, J., Deng, Y., Guo, Z., Chen, Y., 2016. Face Recognition Using Part-Based Dense Sampling Local Features. Neurocomputing, 184, 176-187.

SIMILARITY MEASURE WITH ADAPTIVE LOCAL WINDOW IN DIFFERENT SIZE IMAGES

Year 2017, Volume: 5 Issue: 3, 609 - 614, 21.12.2017
https://doi.org/10.21923/jesd.328160

Abstract

Content based image retrieval methods require
different features such as color, pattern and shape information. The
researchers also use the data obtained from the image histogram in this
context. The histogram information is calculated locally or globally. However,
even though they have the same content, local approaches cannot be used in
images with different aspect ratios, and methods that process over the entire
pixels cannot always give the desired results. In this study, a new method
based on the local histogram, which is used for the similarity measurement of
images, has been developed by providing equal number of windows from two images
of different sizes. The developed method is tested on 100 images on Weizmann
single object image segmentation database and the success of the method is
compared with global histogram approaches.

References

  • Alpert, S., Galun, M., Brandt, A., & Basri, R., 2012. Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration. IEEE transactions on pattern analysis and machine intelligence, 34(2), 315-327.
  • Chitkara, V., 2001. Color-Based Image Retrieval Using Compact Binary Signatures, Tech. Report TR 01-08, University of Alberta Edmonton, Alberta, Canada.
  • Ioka, M., 1989. A Method of Defining the Similarity of Images on the Basis of Color Information, Tech. ReportRT-0030, IBM Tokyo Research Lab.
  • Niblack, C.W., Barber, R., Equitz, V., Flickner, M.D., Glasman, E.H., Petkovic, D., … & Taubin, G., 1993. The QBIC Project: Querying Images by Content Using Color, Texture and Shape, Proc. of IS & T/SPIE Int. Symp. on Electronic Imaging: Science & Technology, 1908: Storage and Retrieval for Image and Video Databases, 173-187.
  • Stricker, M., Dimai, A., 1997. Spectral Covariance and Fuzzy Regions for Image Indexing, Machine Vision Applications, 10(2), 66–73.
  • Stricker, M., Orengo, M., 1995. Similarity of Color Images, Proc. of the SPIE Conf., 2420, 381–392.
  • Swain, M.J., Ballard, D.H., 1991. Color Indexing, Int. J. Computer Vision, 7(1), 11–32.
  • Tanyeri, U., İncetaş, M.O., Aydemir, Z., 2017. Gürültülü Görüntü Üzerinde İnterpolasyon Etkisinin Filtre Yöntemleri ile İncelenmesi, 25. IEEE Sinyal İsleme ve İletişim Uygulamaları Kurultayı.
  • TinEye. (n.d.). Retrieved December 29, 2016, from http://www.tineye.com/faq#similar.
  • Vassilieva, N.S., 2009. Content-based Image Retrieval Methods. Programming and Computer Software, 35(3), 158-180.
  • Vassilieva, N., Novikov, B., 2005. Construction of Correspondences between Low-level Characteristics and Semantics of Static Images, Proc. of the 7th All-Russian Scientific Conf. “Electronic Libraries: Perspective Methods and Technologies, Electronic Collections”, RCDL’2005, Yaroslavl’, Russia.
  • Wang, Z., Lu, L., Bovik, A.C., 2004. Video Quality Assessment Based on Structural Distortion Measurement. Signal processing: Image communication, 19(2), 121-132.
  • What is the Algorithm Used by Google's Reverse Image Search (i.e. search by image)? (n.d.). Retrieved December 29, 2016, from https://www.quora.com/What-is-the-algorithm-used-by-Googles-reverse-image-search-i-e-search-by-image?redirected_qid=828413.
  • Who were They? (n.d.). Retrieved May 24, 2017, from https://www.myheritage.com.tr/
  • Xu, S., Li, C., Jiang, S., Liu, X.P., 2012. Similarity Measures for Content-Based Image Retrieval Based on Intuitionistic Fuzzy Set Theory. JCP, 7(7), 1733-1742.
  • Zhang, J., Deng, Y., Guo, Z., Chen, Y., 2016. Face Recognition Using Part-Based Dense Sampling Local Features. Neurocomputing, 184, 176-187.
There are 16 citations in total.

Details

Subjects Engineering
Journal Section Research Articles
Authors

Mahmut Kılıçaslan 0000-0003-1117-7736

Ufuk Tanyeri 0000-0002-7039-9577

Mürsel Ozan İncetaş This is me 0000-0002-1016-1655

Burcu Yakışır Girgin This is me 0000-0002-1160-4294

Recep Demirci 0000-0002-3278-0078

Cemal Atakan 0000-0001-6943-1675

Publication Date December 21, 2017
Submission Date July 13, 2017
Acceptance Date October 30, 2017
Published in Issue Year 2017 Volume: 5 Issue: 3

Cite

APA Kılıçaslan, M., Tanyeri, U., İncetaş, M. O., Yakışır Girgin, B., et al. (2017). FARKLI BOYUTLARDA GÖRÜNTÜLERDE UYARLAMALI YEREL PENCERE İLE BENZERLİK ÖLÇÜMÜ. Mühendislik Bilimleri Ve Tasarım Dergisi, 5(3), 609-614. https://doi.org/10.21923/jesd.328160