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Detecting visual objects by edge crawling

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Content-based image retrieval methods develop rapidly with a growing scale of image repositories. They are usually based on comparing and indexing some image features. We developed a new algorithm for finding objects in images by traversing their edges. Moreover, we describe the objects by histograms of local features and angles. We use such a description to retrieve similar images fast. We performed extensive experiments on three established image datasets proving the effectiveness of the proposed method.
Rocznik
Strony
223--237
Opis fizyczny
Bibliogr. 45poz., rys.
Twórcy
  • Czestochowa University of Technology, al. Armii Krajowej 36, 42-200 Częstochowa, Poland
  • Institute of Information Technology, Lodz University of Technology, Lodz, Poland
autor
  • School of Computer Science and Engineering, Xi’an University of Technology, China
  • Information Technology Institute, University of Social Sciences, 90-113 Łodz Clark University Worcester, MA 01610, USA
Bibliografia
  • [1] Agarwal, M., Maheshwari, R.: Á trous gradient structure descriptor for content based image retrieval. International Journal of Multimedia Information Retrieval 1(2), 129-138 (2012)
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  • [4] Bao, P., Zhang, L., Wu, X.: Canny edge detection enhancement by scale multiplication. IEEE transactions on pattern analysis and machine intelligence 27(9), 1485-1490 (2005)
  • [5] Bay, H., Tuytelaars, T., Van Gool, L.: Surf: Speeded up robust features. In: European conference on computer vision, pp. 404-417. Springer (2006)
  • [6] Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE transactions on pattern analysis and machine intelligence 24(4), 509-522 (2002)
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  • [8] Das, S., Garg, S., Sahoo, G.: Comparison of content based image retrieval systems using wavelet and curvelet transform. The International Journal of Multimedia & Its Applications 4(4), 137 (2012)
  • [9] Deselaers, T., Keysers, D., Ney, H.: Features for image retrieval: an experimental comparison. Information retrieval 11(2), 77-107 (2008)
  • [10] Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. International Journal of Computer Vision 88(2), 303-338 (2010)
  • [11] Fang, Y., Wang, J., Yuan, Y., Lei, J., Lin, W., Callet, P.L.: Saliency-based stereoscopic image retargeting. Information Sciences 372(Supplement C), 347-358 (2016)
  • [12] Ferdaus, M.M., Anavatti, S.G., Garratt, M.A., Pratam, M.: Development of c-means clustering based adaptive fuzzy controller for a flapping wing micro air vehicle. Journal of Artificial Intelligence and Soft Computing Research 9(2), 99-109 (2019). DOI 10.2478/jaiscr-2018-0027
  • [13] Gabryel, M.: The bag-of-words methods with pareto-fronts for similar image retrieval. In: R. Damaševicius, V. Mikašyt ˇ e (eds.) Information ˙ and Software Technologies, pp. 374-384. SpringerInternational Publishing, Cham (2017)
  • [14] Gabryel, M., Korytkowski, M., Scherer, R., Rutkowski, L.: Object detection by simple fuzzy classifiers generated by boosting. In: L. Rutkowski, M. Korytkowski, R. Scherer, R. Tadeusiewicz, L. Zadeh, J. Zurada (eds.) Artificial Intelligence andSoft Computing, Lecture Notes in Computer Science, vol. 7894, pp. 540-547. Springer Berlin Heidelberg (2013)
  • [15] Gopal, N., Bhooshan, R.S.: Content based image retrieval using enhanced surf. In: 2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing andGraphics (NCVPRIPG), pp. 1-4 (2015). DOI 10.1109/NCVPRIPG.2015.7490035
  • [16] Grossmann, A.: Wavelet transforms and edge detection. In: Stochastic processes in physics and engineering, pp. 149-157. Springer (1988)
  • [17] Grycuk, R.: Novel visual object descriptor using surf and clustering algorithms. Journal of Applied Mathematics and Computational Mechanics 15(3), 37-46 (2016)
  • [18] Grycuk, R., Gabryel, M., Korytkowski, M., Scherer, R.: Content-based image indexing by dataclustering and inverse document frequency. In:Beyond Databases, Architectures and Structures 2014, Communications in Computer and Information Science, pp. 374-383. Springer Berlin Heidelberg (2014). Manuscript accepted for publication
  • [19] Grycuk, R., Gabryel, M., Korytkowski, M., Scherer, R., Romanowski, J.: Improved digital image segmentation based on stereo vision and mean shift algorithm. In: Parallel Processing and Applied Mathematics 2013, Lecture Notes in Computer Science, pp. 433-443. Springer Berlin Heidelberg (2014). Manuscript accepted for publication
  • [20] Grycuk, R., Gabryel, M., Scherer, M., Voloshynovskiy, S.: Image descriptor based on edge detection and crawler algorithm. In: International Conference on Artificial Intelligence and Soft Computing, pp. 647-659. Springer International Publishing (2016)
  • [21] Grycuk, R., Gabryel, M., Scherer, R., Voloshynovskiy, S.: Multi-layer architecture for storing visual data based on wcf and Microsoft sql server database. In: L. Rutkowski, M. Korytkowski, R. Scherer, R. Tadeusiewicz, L.A. Zadeh, J.M. Zurada (eds.) Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science, vol. 9119, pp. 715-726. Springer International Publishing (2015)
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  • [24] Korytkowski, M., Rutkowski, L., Scherer, R.: Fast image classification by boosting fuzzy classifiers. Information Sciences 327, 175-182 (2016)
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  • [28] Lowe, D.G.: Distinctive image features from scaleinvariant keypoints. International journal of computer vision 60(2), 91-110 (2004)
  • [29] Luo, Y., Duraiswami, R.: Canny edge detection on nvidia cuda. In: Computer Vision and Pattern Recognition Workshops, 2008. CVPRW’08. IEEE Computer Society Conference on, pp. 1-8. IEEE (2008)
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  • [31] Memon, M.H., Li, J.P., Memon, I., Arain, Q.A.: Geo matching regions: multiple regions of interests using content based image retrieval based on relative locations. Multimedia Tools and Applications 76(14), 15,377-15,411 (2017)
  • [32] Meskaldji, K., Boucherkha, S., Chikhi, S.: Color quantization and its impact on color histogram based image retrieval accuracy. In: 2009 First International Conference on Networked Digital Technologies, pp. 515-517. IEEE (2009)
  • [33] Murala, S., Maheshwari, R., Balasubramanian, R.: Directional local extrema patterns: a new descriptor for content based image retrieval. International journal of multimedia information retrieval 1(3), 191-203 (2012)
  • [34] Park, D.K., Jeon, Y.S., Won, C.S.: Efficient use of local edge histogram descriptor. In: Proceedings of the 2000 ACM Workshops on Multimedia, MULTIMEDIA ’00, pp. 51-54. ACM, New York, NY, USA (2000). DOI 10.1145/357744.357758.http://doi.acm.org/10.1145/357744.357758
  • [35] Saadatmand-Tarzjan, M., Moghaddam, H.A.: A novel evolutionary approach for optimizing contentbased image indexing algorithms. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 37(1), 139-153 (2007)
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  • [38] Sumana, I.J., Islam, M.M., Zhang, D., Lu, G.: Content based image retrieval using curvelet transform. In: Multimedia Signal Processing, 2008 IEEE 10th Workshop on, pp. 11-16. IEEE (2008)
  • [39] Šváb, J., Krajník, T., Faigl, J., Pˇreucil, L.: Fpga based speeded up robust features. In: Technologies for Practical Robot Applications, 2009. TePRA 2009. IEEE International Conference on, pp. 35-41. IEEE (2009)
  • [40] Tao, D.: The corel database for content based image retrieval (2009)
  • [41] Ting, K.M.: Precision and recall. In: Encyclopedia of machine learning, pp. 781-781. Springer (2011)
  • [42] Walia, E., Pal, A.: Fusion framework for effective color image retrieval. Journal of Visual Communication and Image Representation 25(6), 1335-1348 (2014)
  • [43] Wang, C., Zhang, B., Qin, Z., Xiong, J.: Spatial weighting for bag-of-features based image retrieval. In: International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, pp. 91-100. Springer (2013)
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  • [45] Wang, L., Chang, Y., Wang, H., Wu, Z., Pu, J., Yang, X.: An active contour model based on local fitted images for image segmentation. Information Sciences 418-419(Supplement C), 61-73 (2017)
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bfa5856f-5480-4678-93ec-631968b2968c
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