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The Bag-of-Words Method with Dictionary Analysis by Evolutionary Algorithm

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Artificial Intelligence and Soft Computing (ICAISC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10246))

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Abstract

In this paper we present innovative solutions improving general operational efficiency of the Bag-of-Words algorithm (BoW). The first innovation which we put forward is creating a visual words’ dictionary using the clustering algorithm which in itself is responsible for selecting the appropriate number of clusters. This solution results in significant automation of image database creation. Another innovation is adding to the BoW model an analytical module whose task is to analyse the visual words’ dictionary and to modify histogram values before storing them in a database. This algorithm is operated with the use of the evolutionary algorithm. The modifications of the BoW algorithm significantly improve the efficiency of image search and classification, which has been presented in a variety of experiments.

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References

  1. Almohammadi, K., Hagras, H., Alghazzawi, D., Aldabbagh, G.: Users-centric adaptive learning system based on interval type-2 fuzzy logic for massively crowded e-learning platforms. J. Artif. Intell. Soft Comput. Res. 6(2), 81–101 (2016)

    Article  Google Scholar 

  2. Audet, S.: JavaCV (2017). http://bytedeco.org/. Online; Accessed 1 Feb 2017

  3. 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

    Chapter  Google Scholar 

  4. Bilski, J., Smolag, J.: Parallel architectures for learning the RTRN and elman dynamic neural networks. IEEE Trans. Parallel Distrib. Syst. 26(9), 2561–2570 (2015)

    Article  Google Scholar 

  5. Bradski, G.: The OpenCV library. Dr. Dobb’s J. Softw. Tools 25, 120–126 (2000)

    Google Scholar 

  6. Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, pp. 1–22 (2004)

    Google Scholar 

  7. Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. In: Conference on Computer Vision and Pattern Recognition Workshop, CVPRW 2004, pp. 178–178 (2004)

    Google Scholar 

  8. Fritzke, B.: Growing grid – a self-organizing network with constant neighborhood range and adaptation strength. Neural Process. Lett. 2(5), 9–13 (1995)

    Article  Google Scholar 

  9. Gabryel, M., Grycuk, R., Korytkowski, M., Holotyak, T.: Image indexing and retrieval using GSOM algorithm. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2015. LNCS, vol. 9119, pp. 706–714. Springer, Cham (2015). doi:10.1007/978-3-319-19324-3_63

    Chapter  Google Scholar 

  10. Gao, H., Dou, L., Chen, W., Sun, J.: Image classification with bag-of-words model based on improved sift algorithm. In: 2013 9th Asian Control Conference (ASCC), pp. 1–6 (2013)

    Google Scholar 

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

    Article  Google Scholar 

  12. Lan, K., Sekiyama, K.: Autonomous viewpoint selection of robot based on aesthetic evaluation of a scene. J. Artif. Intell. Soft Comput. Res. 6(4), 255–265 (2016)

    Article  Google Scholar 

  13. Łapa, K., Cpałka, K., Wang, L.: New method for design of fuzzy systems for nonlinear modelling using different criteria of interpretability. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014. LNCS, vol. 8467, pp. 217–232. Springer, Cham (2014). doi:10.1007/978-3-319-07173-2_20

    Chapter  Google Scholar 

  14. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2169–2178 (2006)

    Google Scholar 

  15. Li, F.F., Perona, P.: A bayesian hierarchical model for learning natural scene categories. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 2, pp. 524–531. IEEE Computer Society (2005)

    Google Scholar 

  16. Li, W., Dong, P., Xiao, B., Zhou, L.: Object recognition based on the region of interest and optimal bag of words model. Neurocomputing 172, 271–280 (2016)

    Article  Google Scholar 

  17. Lin, W.C., Tsai, C.F., Chen, Z.Y., Ke, S.W.: Keypoint selection for efficient bag-of-words feature generation and effective image classification. Inf. Sci. 329, 33–51 (2016)

    Article  Google Scholar 

  18. Liu, J.: Image retrieval based on bag-of-words model. CoRR abs/1304.5168 (2013). http://arxiv.org/abs/1304.5168

  19. Olson, D.L., Delen, D.: Advanced Data Mining Techniques, 1st edn. Springer Publishing Company Incorporated, Heidelberg (2008)

    MATH  Google Scholar 

  20. Pabiasz, S., Starczewski, J.T., Marvuglia, A.: SOM vs FCM vs PCA in 3D face recognition. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2015. LNCS, vol. 9120, pp. 120–129. Springer, Cham (2015). doi:10.1007/978-3-319-19369-4_12

    Chapter  Google Scholar 

  21. Prasad, M., Liu, Y.T., Li, D.L., Lin, C.T., Shah, R.R., Kaiwartya, O.P.: A new mechanism for data visualization with tsk-type preprocessed collaborative fuzzy rule based system. J. Artif. Intell. Soft Comput. Res. 7(1), 33–46 (2017)

    Article  Google Scholar 

  22. Starczewski, A.: A clustering method based on the modified RS validity index. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013. LNCS, vol. 7895, pp. 242–250. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38610-7_23

    Chapter  Google Scholar 

  23. Starczewski, J.T., Pabiasz, S., Vladymyrska, N., Marvuglia, A., Napoli, C., Woźniak, M.: Self organizing maps for 3D face understanding. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS, vol. 9693, pp. 210–217. Springer, Cham (2016). doi:10.1007/978-3-319-39384-1_19

    Google Scholar 

  24. Staszewski, P., Woldan, P., Korytkowski, M., Scherer, R., Wang, L.: Query-by-example image retrieval in microsoft SQL server. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS, vol. 9693, pp. 746–754. Springer, Cham (2016). doi:10.1007/978-3-319-39384-1_66

    Google Scholar 

  25. Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  26. Szarek, A., Korytkowski, M., Rutkowski, L., Scherer, R., Szyprowski, J.: Application of neural networks in assessing changes around implant after total hip arthroplasty. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012. LNCS, vol. 7268, pp. 335–340. Springer, Heidelberg (2012). doi:10.1007/978-3-642-29350-4_40

    Chapter  Google Scholar 

  27. Woźniak, M.: Novel image correction method based on swarm intelligence approach. In: Dregvaite, G., Damasevicius, R. (eds.) ICIST 2016. CCIS, vol. 639, pp. 404–413. Springer, Cham (2016). doi:10.1007/978-3-319-46254-7_32

    Chapter  Google Scholar 

  28. Wozniak, M., Polap, D.: On manipulation of initial population search space in heuristic algorithm through the use of parallel processing approach. In: 2016 IEEE Symposium Series on Computational Intelligence, pp. 1–6. IEEE (2016)

    Google Scholar 

  29. Wozniak, M., Polap, D., Napoli, C., Tramontana, E.: Graphic object feature extraction system based on cuckoo search algorithm. Expert Syst. Appl. 66, 20–31 (2016)

    Article  Google Scholar 

  30. Yin, Z., O’Sullivan, C., Brabazon, A.: An analysis of the performance of genetic programming for realised volatility forecasting. J. Artif. Intell. Soft Comput. Res. 6(3), 155–172 (2016)

    Article  Google Scholar 

  31. Zalasiński, M., Cpałka, K.: New algorithm for on-line signature verification using characteristic hybrid partitions. In: Wilimowska, Z., Borzemski, L., Grzech, A., Świątek, J. (eds.) Information Systems Architecture and Technology: Proceedings of 36th International Conference on Information Systems Architecture and Technology – ISAT 2015 – Part IV. AISC, vol. 432, pp. 147–157. Springer, Cham (2016). doi:10.1007/978-3-319-28567-2_13

    Google Scholar 

  32. Zalasiński, M., Cpałka, K., Er, M.J.: New method for dynamic signature verification using hybrid partitioning. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014. LNCS, vol. 8468, pp. 216–230. Springer, Cham (2014). doi:10.1007/978-3-319-07176-3_20

    Chapter  Google Scholar 

  33. Zhao, C., Li, X., Cang, Y.: Bisecting k-means clustering based face recognition using block-based bag of words model. Optik - Int. J. Light Electron Opt. 126(19), 1761–1766 (2015)

    Article  Google Scholar 

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Correspondence to Marcin Gabryel .

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Gabryel, M., Capizzi, G. (2017). The Bag-of-Words Method with Dictionary Analysis by Evolutionary Algorithm. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10246. Springer, Cham. https://doi.org/10.1007/978-3-319-59060-8_5

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  • DOI: https://doi.org/10.1007/978-3-319-59060-8_5

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