Öz
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.
Anahtar Kelimeler
Histogram karşılaştırma İçerik Tabanlı Görüntü Erişimi Yerel pencere
Kaynakça
- 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.
Öz
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.
Anahtar Kelimeler
Histogram comparison Content based image retrieval Local window
Kaynakça
- 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.
Ayrıntılar
| Konular | Mühendislik |
|---|---|
| Bölüm | Araştırma Makaleleri \ Research Articles |
| Yazarlar | |
| Yayımlanma Tarihi | 21 Aralık 2017 |
| Gönderilme Tarihi | 13 Temmuz 2017 |
| Kabul Tarihi | 30 Ekim 2017 |
| Yayımlandığı Sayı | Yıl 2017 Cilt: 5 Sayı: 3 |