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2017 | OriginalPaper | Buchkapitel

Local Keypoint-Based Image Detector with Object Detection

verfasst von : Rafał Grycuk, Magdalena Scherer, Sviatoslav Voloshynovskiy

Erschienen in: Artificial Intelligence and Soft Computing

Verlag: Springer International Publishing

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Abstract

Accurate and efficient image content description is crucial for image retrieval systems. In the paper we propose a novel method to describe images by a combination of the SURF local keypoint detector and the Canny edge detector. Then, a crawler is used to detect objects. The experiments performed on state-of-the-art image dataset showed that the method generates less data than standalone local keypoint detectors.

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Vorheriges Kapitel The Image Classification with Different Types of Image Features

Nächstes Kapitel Heavy Changes in the Input Flow for Learning Geography of a Robot Environment

Akusok, A., Miche, Y., Karhunen, J., Bjork, K.M., Nian, R., Lendasse, A.: Arbitrary category classification of websites based on image content. IEEE Comput. Intell. Mag. 10(2), 30–41 (2015)CrossRef

Bao, P., Zhang, L., Wu, X.: Canny edge detection enhancement by scale multiplication. IEEE Trans. Pattern Anal. Mach. Intell. 27(9), 1485–1490 (2005)CrossRef

Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRef

Bay, H., Tuytelaars, T., Gool, L.: SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). doi:10.1007/11744023_32 CrossRef

Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)

Chang, T., Kuo, C.C.: Texture analysis and classification with tree-structured wavelet transform. IEEE Trans. Image Process. 2(4), 429–441 (1993)CrossRef

Ding, L., Goshtasby, A.: On the canny edge detector. Pattern Recogn. 34(3), 721–725 (2001)CrossRefMATH

Drozda, P., Sopyła, K., Górecki, P.: Online crowdsource system supporting ground truth datasets creation. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013. LNCS, vol. 7894, pp. 532–539. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38658-9_48 CrossRef

Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)CrossRef

10.

Francos, J., Meiri, A., Porat, B.: A unified texture model based on a 2-d wold-like decomposition. IEEE Trans. Sig. Process. 41(8), 2665–2678 (1993)CrossRefMATH

11.

Grycuk, R., Gabryel, M., Korytkowski, M., Romanowski, J., Scherer, R.: Improved digital image segmentation based on stereo vision and mean shift algorithm. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds.) PPAM 2013. LNCS, vol. 8384, pp. 433–443. Springer, Heidelberg (2014). doi:10.1007/978-3-642-55224-3_41 CrossRef

12.

Grycuk, R., Gabryel, M., Korytkowski, M., Scherer, R.: Content-based image indexing by data clustering and inverse document frequency. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2014. CCIS, vol. 424, pp. 374–383. Springer, Cham (2014). doi:10.1007/978-3-319-06932-6_36 CrossRef

13.

Grycuk, R., Gabryel, M., Korytkowski, M., Scherer, R., Voloshynovskiy, S.: From single image to list of objects based on edge and blob detection. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014. LNCS, vol. 8468, pp. 605–615. Springer, Cham (2014). doi:10.1007/978-3-319-07176-3_53 CrossRef

14.

Grycuk, R., Gabryel, M., Scherer, R., Voloshynovskiy, S.: Multi-layer architecture for storing visual data based on WCF and microsoft SQL server database. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2015. LNCS, vol. 9119, pp. 715–726. Springer, Cham (2015). doi:10.1007/978-3-319-19324-3_64 CrossRef

15.

Grycuk, R., Gabryel, M., Scherer, M., Voloshynovskiy, S.: Image descriptor based on edge detection and Crawler algorithm. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS, vol. 9693, pp. 647–659. Springer, Cham (2016). doi:10.1007/978-3-319-39384-1_57

16.

Grycuk, R., Scherer, R., Gabryel, M.: New image descriptor from edge detector and blob extractor. J. Appl. Math. Comput. Mech. 14(4), 31–39 (2015)CrossRef

17.

Huang, J., Kumar, S., Mitra, M., Zhu, W.J., Zabih, R.: Image indexing using color correlograms. In: Proceedings of 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 762–768, June 1997

18.

Jagadish, H.V.: A retrieval technique for similar shapes. SIGMOD Rec. 20(2), 208–217 (1991)CrossRef

19.

Jain, A.K., Farrokhnia, F.: Unsupervised texture segmentation using Gabor filters. Pattern Recogn. 24(12), 1167–1186 (1991)CrossRef

20.

Jégou, H., Douze, M., Schmid, C., Pérez, P.: Aggregating local descriptors into a compact image representation. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3304–3311. IEEE (2010)

21.

Jégou, H., Perronnin, F., Douze, M., Sanchez, J., Perez, P., Schmid, C.: Aggregating local image descriptors into compact codes. IEEE Trans. Pattern Anal. Mach. Intell. 34(9), 1704–1716 (2012)CrossRef

22.

Kanimozhi, T., Latha, K.: An integrated approach to region based image retrieval using firefly algorithm and support vector machine. Neurocomputing 151, 1099–1111 (2015). Part 3(0)

23.

Karakasis, E., Amanatiadis, A., Gasteratos, A., Chatzichristofis, S.: Image moment invariants as local features for content based image retrieval using the bag-of-visual-words model. Pattern Recogn. Lett. 55, 22–27 (2015)CrossRef

24.

Kauppinen, H., Seppanen, T., Pietikainen, M.: An experimental comparison of autoregressive and Fourier-based descriptors in 2d shape classification. IEEE Trans. Pattern Anal. Mach. Intell. 17(2), 201–207 (1995)CrossRef

25.

Kiranyaz, S., Birinci, M., Gabbouj, M.: Perceptual color descriptor based on spatial distribution: a top-down approach. Image Vision Comput. 28(8), 1309–1326 (2010)CrossRef

26.

Korytkowski, M.: Novel visual information indexing in relational databases. Integr. Comput.-Aid. Eng. 24(2), 119–128 (2017)CrossRef

27.

Korytkowski, M., Rutkowski, L., Scherer, R.: Fast image classification by boosting fuzzy classifiers. Inf. Sci. 327, 175–182 (2016)MathSciNetCrossRef

28.

Li, X., Jiang, J., Fan, Q.: An improved real-time hardware architecture for canny edge detection based on FPGA. In: 2012 Third International Conference on Intelligent Control and Information Processing (ICICIP), pp. 445–449. IEEE (2012)

29.

Lin, C.H., Chen, H.Y., Wu, Y.S.: Study of image retrieval and classification based on adaptive features using genetic algorithm feature selection. Expert Syst. Appl. 41(15), 6611–6621 (2014)CrossRef

30.

Liu, G.H., Yang, J.Y.: Content-based image retrieval using color difference histogram. Pattern Recogn. 46(1), 188–198 (2013)MathSciNetCrossRef

31.

Liu, S., Bai, X.: Discriminative features for image classification and retrieval. Pattern Recogn. Lett. 33(6), 744–751 (2012)CrossRef

32.

Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157. IEEE (1999)

33.

Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRef

34.

Luo, Y.M., Duraiswami, R.: Canny edge detection on nvidia cuda. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2008, pp. 1–8. IEEE (2008)

35.

Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image Vis. Comput. 22(10), 761–767 (2004). British Machine Vision Computing 2002

36.

Meskaldji, K., Boucherkha, S., Chikhi, S.: Color quantization and its impact on color histogram based image retrieval accuracy. In: First International Conference on Networked Digital Technologies, NDT 2009, pp. 515–517, July 2009

37.

Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. Int. J. Comput. Vis. 60(1), 63–86 (2004)CrossRef

38.

Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006, vol. 2, pp. 2161–2168. IEEE Computer Society, Washington, DC (2006)

39.

Ogawa, K., Ito, Y., Nakano, K.: Efficient canny edge detection using a gpu. In: 2010 First International Conference on Networking and Computing (ICNC), pp. 279–280. IEEE (2010)

40.

Pass, G., Zabih, R.: Histogram refinement for content-based image retrieval. In: Proceedings 3rd IEEE Workshop on Applications of Computer Vision, WACV 1996, pp. 96–102, December 1996

41.

Patgiri, C., Sarma, M., Sarma, K.K.: A class of neuro-computational methods for assamese fricative classification. J. Artif. Intell. Soft Comput. Res. 5(1), 59–70 (2015)CrossRef

42.

Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: A simultaneous feature adaptation and feature selection method for content-based image retrieval systems. Knowl.-Based Syst. 39, 85–94 (2013)CrossRef

43.

Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: Orb: an efficient alternative to sift or surf. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2564–2571, November 2011

44.

Scherer, R.: Multiple Fuzzy Classification Systems. Springer, Berlin (2012)

45.

Shrivastava, N., Tyagi, V.: Content based image retrieval based on relative locations of multiple regions of interest using selective regions matching. Inf. Sci. 259, 212–224 (2014)CrossRef

46.

Sivic, J., Zisserman, A.: Video google: a text retrieval approach to object matching in videos. In: Proceedings of Ninth IEEE International Conference on Computer Vision, vol. 2, pp. 1470–1477, October 2003

47.

Stanovov, V., Semenkin, E., Semenkina, O.: Self-configuring hybrid evolutionary algorithm for fuzzy imbalanced classification with adaptive instance selection. J. Artif. Intell. Soft Comput. Res. 6(3), 173–188 (2016)CrossRef

48.

Šváb, J., Krajník, T., Faigl, J., Přeučil, L.: FPGA based speeded up robust features. In: IEEE International Conference on Technologies for Practical Robot Applications, TPRA 2009, pp. 35–41. IEEE (2009)

49.

Terriberry, T.B., French, L.M., Helmsen, J.: GPU accelerating speeded-up robust features. In: Proceedings of 3DPVT, vol. 8, pp. 355–362 (2008)

50.

Veltkamp, R.C., Hagedoorn, M.: State of the art in shape matching. In: Lew, M.S. (ed.) Principles of Visual Information Retrieval, pp. 87–119. Springer, London (2001)CrossRef

51.

Villmann, T., Bohnsack, A., Kaden, M.: Can learning vector quantization be an alternative to SVM and deep learning? - recent trends and advanced variants of learning vector quantization for classification learning. J. Artif. Intell. Soft Comput. Res. 7(1), 65–81 (2017)CrossRef

52.

Wang, B., Fan, S.: An improved canny edge detection algorithm. In: 2009 Second International Workshop on Computer Science and Engineering, pp. 497–500. IEEE (2009)

53.

Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1794–1801, June 2009

54.

Zitnick, C.L., Dollár, P.: Edge boxes: locating object proposals from edges. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 391–405. Springer, Cham (2014). doi:10.1007/978-3-319-10602-1_26

Titel: Local Keypoint-Based Image Detector with Object Detection
verfasst von: Rafał Grycuk
Magdalena Scherer
Sviatoslav Voloshynovskiy
Verlag: Springer International Publishing
Buch: Artificial Intelligence and Soft Computing
Print ISBN: 978-3-319-59062-2

Electronic ISBN: 978-3-319-59063-9

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-59063-9_45

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