Cite

[1] Alharbi, A., Tchier, F.: Using a genetic-fuzzy algorithm as a computer aided diagnosis tool on saudi arabian breast cancer database. Mathematical Biosciences 286, 39 – 48 (2017)10.1016/j.mbs.2017.02.00228185926Search in Google Scholar

[2] Antonelli, M., Ducange, P., Marcelloni, F.: A fast and efficient multi-objective evolutionary learning scheme for fuzzy rule-based classifiers. Information Sciences 283, 36 – 54 (2014). New Trend of Computational Intelligence in Human-Robot Interaction10.1016/j.ins.2014.06.014Search in Google Scholar

[3] Awad, N.H., Ali, M.Z., Suganthan, P.N., Reynolds, R.G.: An ensemble sinusoidal parameter adaptation incorporated with L-SHADE for solving CEC2014 benchmark problems. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 2958–2965. IEEE (2016)10.1109/CEC.2016.7744163Search in Google Scholar

[4] Aydogan, E.K., Karaoglan, I., Pardalos, P.M.: hga: Hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems. Applied Soft Computing 12(2), 800 – 806 (2012)10.1016/j.asoc.2011.10.010Open DOISearch in Google Scholar

[5] Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3), 346–359 (2008)10.1016/j.cviu.2007.09.014Search in Google Scholar

[6] Beloufa, F., Chikh, M.: Design of fuzzy classifier for diabetes disease using modified artificial bee colony algorithm. Computer Methods and Programs in Biomedicine 112(1), 92 – 103 (2013)10.1016/j.cmpb.2013.07.00923932385Search in Google Scholar

[7] Berlanga, F., Rivera, A., del Jesus, M., Herrera, F.: Gp-coach: Genetic programming-based learning of {COmpact} and {ACcurate} fuzzy rule-based classification systems for high-dimensional problems. Information Sciences 180(8), 1183 – 1200 (2010)10.1016/j.ins.2009.12.020Search in Google Scholar

[8] Bradski, G.: The opencv library. Doctor Dobbs Journal 25(11), 120–126 (2000)Search in Google Scholar

[9] Brest, J., Greiner, S., Bošković, B., Mernik, M., Bošković, V.: Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems. IEEE Transactions on Evolutionary Computation 10(6), 646–657 (2006)10.1109/TEVC.2006.872133Search in Google Scholar

[10] Brest, J., Maučec, M.S., Bošković, B.: il-shade: Improved l-shade algorithm for single objective real-parameter optimization. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 1188–1195. IEEE (2016)10.1109/CEC.2016.7743922Search in Google Scholar

[11] Brest, J., Maučec, M.S., Bošković, B.: Single objective real-parameter optimization: Algorithm jSO. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 1311–1318. IEEE (2017)10.1109/CEC.2017.7969456Search in Google Scholar

[12] Casillas, J., Cordon, O., del Jesus, M.J., Herrera, F.: Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction. IEEE Transactions on Fuzzy Systems 13(1), 13–29 (2005)10.1109/TFUZZ.2004.839670Search in Google Scholar

[13] Casillas, J., Cordón, O., Herrera, F.: Learning fuzzy rules using ant colony optimization algorithms. In: Abstract proceedings of ANTS2000 From Ant Colonies to Arti Ants: A Series of International Workshops on Ant Algorithms, pp. 13–21 (2000)Search in Google Scholar

[14] Chang, T., Kuo, C.C.: Texture analysis and classification with tree-structured wavelet transform. Image Processing, IEEE Transactions on 2(4), 429–441 (1993). DOI 10.1109/83.24235310.1109/83.242353Open DOISearch in Google Scholar

[15] Cordón, O.: A historical review of evolutionary learning methods for mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems. International Journal of Approximate Reasoning 52(6), 894 – 913 (2011)10.1016/j.ijar.2011.03.004Open DOISearch in Google Scholar

[16] Cordón, O., Gomide, F., Herrera, F., Hoffmann, F., Magdalena, L.: Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets and Systems 141(1), 5 – 31 (2004)10.1016/S0165-0114(03)00111-8Search in Google Scholar

[17] Das, S., Abraham, A., Chakraborty, U.K., Konar, A.: Differential Evolution Using a Neighborhood-Based Mutation Operator. IEEE Transactions on Evolutionary Computation 13(3), 526–553 (2009)10.1109/TEVC.2008.2009457Search in Google Scholar

[18] Das, S., Mullick, S.S., Suganthan, P.N.: Recent advances in differential evolution – An updated survey. Swarm and Evolutionary Computation 27, 1–30 (2016)10.1016/j.swevo.2016.01.004Search in Google Scholar

[19] Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.S.: Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the Twentieth Annual Symposium on Computational Geometry, SCG ’04, pp. 253–262. ACM, New York, NY, USA (2004)10.1145/997817.997857Search in Google Scholar

[20] Elhag, S., Fernández, A., Bawakid, A., Alshomrani, S., Herrera, F.: On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on intrusion detection systems. Expert Systems with Applications 42(1), 193 – 202 (2015)10.1016/j.eswa.2014.08.002Search in Google Scholar

[21] 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)10.1007/s11263-009-0275-4Open DOISearch in Google Scholar

[22] Fernández, A., Herrera, F.: Evolutionary Fuzzy Systems: A Case Study in Imbalanced Classification, pp. 169–200. Springer International Publishing, Cham (2016)10.1007/978-3-319-30421-2_12Search in Google Scholar

[23] Francos, J., Meiri, A., Porat, B.: A unified texture model based on a 2-d wold-like decomposition. Signal Processing, IEEE Transactions on 41(8), 2665–2678 (1993). DOI 10.1109/78.22989710.1109/78.229897Search in Google Scholar

[24] Freischlad, M., Schnellenbach-Held, M.: A machine learning approach for the support of preliminary structural design. Advanced Engineering Informatics 19(4), 281 – 287 (2005)10.1016/j.aei.2005.07.001Open DOISearch in Google Scholar

[25] Freischlad, M., Schnellenbach-Held, M., Pull-mann, T.: Evolutionary generation of implicative fuzzy rules for design knowledge representation. In: I. Smith (ed.) Intelligent Computing in Engineering and Architecture, Lecture Notes in Computer Science, vol. 4200, pp. 222–229. Springer Berlin Heidelberg (2006)10.1007/11888598_22Search in Google Scholar

[26] Gorzałczany, M.B., Rudziński, F.: Interpretable and accurate medical data classification – a multi-objective genetic-fuzzy optimization approach. Expert Systems with Applications 71, 26 – 39 (2017)10.1016/j.eswa.2016.11.017Open DOISearch in Google Scholar

[27] Grauman, K., Darrell, T.: Efficient image matching with distributions of local invariant features. In: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, vol. 2, pp. 627–634 vol. 2 (2005). DOI 10.1109/CVPR.2005.13810.1109/CVPR.2005.138Open DOISearch in Google Scholar

[28] Guo, S.M., Tsai, J.S.H., Yang, C.C., Hsu, P.H.: A self-optimization approach for L-SHADE incorporated with eigenvector-based crossover and successful-parent-selecting framework on CEC 2015 benchmark set. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 1003–1010. IEEE (2015)10.1109/CEC.2015.7256999Search in Google Scholar

[29] Huang, J., Kumar, S., Mitra, M., Zhu, W.J., Zabih, R.: Image indexing using color correlo-grams. In: Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on, pp. 762–768 (1997). DOI 10.1109/CVPR.1997.60941210.1109/CVPR.1997.609412Open DOISearch in Google Scholar

[30] Jagadish, H.V.: A retrieval technique for similar shapes. SIGMOD Rec. 20(2), 208–217 (1991)10.1145/119995.115821Open DOISearch in Google Scholar

[31] Jain, A.K., Farrokhnia, F.: Unsupervised texture segmentation using gabor filters. Pattern Recognition 24(12), 1167 – 1186 (1991)10.1016/0031-3203(91)90143-SSearch in Google Scholar

[32] Kauppinen, H., Seppanen, T., Pietikainen, M.: An experimental comparison of autoregressive and fourier-based descriptors in 2d shape classification. Pattern Analysis and Machine Intelligence, IEEE Transactions on 17(2), 201–207 (1995). DOI 10.1109/34.36816810.1109/34.368168Open DOISearch in Google Scholar

[33] 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)10.1016/j.imavis.2010.01.012Search in Google Scholar

[34] Koshiyama, A., Escovedo, T., Dias, D., Vellasco, M., Tanscheit, R.: Gpf-class: A genetic fuzzy model for classification. In: Evolutionary Computation (CEC), 2013 IEEE Congress on, pp. 3275–3282 (2013). DOI 10.1109/CEC.2013.655797110.1109/CEC.2013.6557971Open DOISearch in Google Scholar

[35] Krömer, P., Platos, J.: Simultaneous prediction of wind speed and direction by evolutionary fuzzy rule forest. In: International Conference on Computational Science, ICCS 2017, 12-14 June 2017, Zurich, Switzerland, pp. 295–304 (2017). DOI 10.1016/j.procs.2017.05.195. URL https://doi.org/10.1016/j.procs.2017.05.19510.1016/j.procs.2017.05.195.URLhttps://doi.org/10.1016/j.procs.2017.05.195Open DOISearch in Google Scholar

[36] Krömer, P., Prauzek, M., Stankuš, M., Konečn`y, J.: Adaptive fuzzy video compression control for advanced driver assistance systems. In: 2018 26th International Conference on Systems Engineering (ICSEng), pp. 1–9. IEEE (2018)10.1109/ICSENG.2018.8638184Search in Google Scholar

[37] Liang, S., Kuo, C., Shaw, F., Chen, Y., Hsu, C., Chen, J.: Combination of expert knowledge and a genetic fuzzy inference system for automatic sleep staging. IEEE Transactions on Biomedical Engineering 63(10), 2108–2118 (2016). DOI 10.1109/TBME.2015.251036510.1109/TBME.2015.251036526700856Search in Google Scholar

[38] Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)10.1023/B:VISI.0000029664.99615.94Open DOISearch in Google Scholar

[39] Mallipeddi, R., Suganthan, P.N., Pan, Q.K., Tasgetiren, M.F.: Differential evolution algorithm with ensemble of parameters and mutation strategies. Applied Soft Computing 11(2), 1679–1696 (2011)10.1016/j.asoc.2010.04.024Open DOISearch in Google Scholar

[40] Marinaki, M., Marinakis, Y., Stavroulakis, G.E.: Fuzzy control optimized by a multi-objective differential evolution algorithm for vibration suppression of smart structures. Computers & Structures 147, 126–137 (2015)10.1016/j.compstruc.2014.09.018Search in Google Scholar

[41] Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image and Vision Computing 22(10), 761 – 767 (2004). British Machine Vision Computing 200210.1016/j.imavis.2004.02.006Search in Google Scholar

[42] Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. International Journal of Computer Vision 60(1), 63–86 (2004)10.1023/B:VISI.0000027790.02288.f2Search in Google Scholar

[43] Mininno, E., Neri, F., Cupertino, F., Naso, D.: Compact Differential Evolution. IEEE Transactions on Evolutionary Computation 15(1), 32–54 (2011)10.1109/TEVC.2010.2058120Open DOISearch in Google Scholar

[44] Nguyen, T., Khosravi, A., Creighton, D., Nahavandi, S.: Classification of healthcare data using genetic fuzzy logic system and wavelets. Expert Systems with Applications 42(4), 2184 – 2197 (2015)10.1016/j.eswa.2014.10.027Open DOISearch in Google Scholar

[45] 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 - Volume 2, CVPR ’06, pp. 2161–2168. IEEE Computer Society, Washington, DC, USA (2006)Search in Google Scholar

[46] Opara, K.R., Arabas, J.: Differential Evolution: A survey of theoretical analyses. Swarm and Evolutionary Computation (2018).. DOI: https://doi.org/10.1016/j.swevo.2018.06.01010.1016/j.swevo.2018.06.010Open DOISearch in Google Scholar

[47] Pass, G., Zabih, R.: Histogram refinement for content-based image retrieval. In: Applications of Computer Vision, 1996. WACV ’96., Proceedings 3rd IEEE Workshop on, pp. 96–102 (1996). DOI 10.1109/ACV.1996.57200810.1109/ACV.1996.572008Open DOISearch in Google Scholar

[48] Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: Computer Vision and Pattern Recognition, 2007. CVPR ’07. IEEE Conference on, pp. 1–8 (2007)10.1109/CVPR.2007.383172Search in Google Scholar

[49] Piotrowski, A.P.: aL-SHADE optimization algorithms with population-wide inertia. Information Sciences (2018)10.1016/j.ins.2018.08.030Search in Google Scholar

[50] Piotrowski, A.P., Napiorkowski, J.J.: Some meta-heuristics should be simplified. Information Sciences 427, 32–62 (2018)10.1016/j.ins.2017.10.039Search in Google Scholar

[51] Piotrowski, A.P., Napiorkowski, J.J.: Step-by-step improvement of JADE and SHADE-based algorithms: Success or failure? Swarm and Evolutionary Computation (2018).. DOI: https://doi.org/10.1016/j.swevo.2018.03.00710.1016/j.swevo.2018.03.007Open DOISearch in Google Scholar

[52] Pratama, M., Pedrycz, W., Webb, G.I.: An incremental construction of deep neuro fuzzy system for continual learning of non-stationary data streams. CoRR abs/1808.08517 (2018). URL http://arxiv.org/abs/1808.08517Search in Google Scholar

[53] Prauzek, M., Krömer, P., Rodway, J., Musilek, P.: Differential evolution of fuzzy controller for environmentally-powered wireless sensors. Applied Soft Computing 48, 193–206 (2016)10.1016/j.asoc.2016.06.040Search in Google Scholar

[54] Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization. IEEE Transactions on Evolutionary Computation 13(2), 398–417 (2009)10.1109/TEVC.2008.927706Open DOISearch in Google Scholar

[55] Qu, B.Y., Suganthan, P.N., Liang, J.J.: Differential Evolution With Neighborhood Mutation for Multi-modal Optimization. IEEE Transactions on Evolutionary Computation 16(5), 601–614 (2012)10.1109/TEVC.2011.2161873Open DOISearch in Google Scholar

[56] Rey, M., Galende, M., Fuente, M., Sainz-Palmero, G.: Multi-objective based fuzzy rule based systems (frbss) for trade-off improvement in accuracy and interpretability: A rule relevance point of view. Knowledge-Based Systems 127, 67 – 84 (2017)10.1016/j.knosys.2016.12.028Search in Google Scholar

[57] Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: Orb: An efficient alternative to sift or surf. In: Computer Vision (ICCV), 2011 IEEE International Conference on, pp. 2564–2571 (2011). DOI 10.1109/ICCV.2011.612654410.1109/ICCV.2011.6126544Open DOISearch in Google Scholar

[58] Rudziński, F.: A multi-objective genetic optimization of interpretability-oriented fuzzy rule-based classifiers. Applied Soft Computing 38, 118 – 133 (2016)10.1016/j.asoc.2015.09.038Open DOISearch in Google Scholar

[59] Rutkowski, L.: Computational Intelligence Methods and Techniques. Springer Berlin Heidelberg (2008)Search in Google Scholar

[60] Schapire, R.E.: A brief introduction to boosting. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence - Volume 2, IJCAI’99, pp. 1401–1406. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999)Search in Google Scholar

[61] Scherer, R.: Designing boosting ensemble of relational fuzzy systems. International Journal of Neural Systems 20(5), 381–388 (2010)2094551710.1142/S012906571000252820945517Search in Google Scholar

[62] Scherer, R.: Multiple Fuzzy Classification Systems. Springer Publishing Company, Incorporated (2014)Search in Google Scholar

[63] Simo-Serra, E., Trulls, E., Ferraz, L., Kokkinos, I., Fua, P., Moreno-Noguer, F.: Discriminative learning of deep convolutional feature point descriptors. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 118–126 (2015)10.1109/ICCV.2015.22Search in Google Scholar

[64] Sivic, J., Zisserman, A.: Video google: a text retrieval approach to object matching in videos. In: Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on, pp. 1470–1477 vol.2 (2003)10.1109/ICCV.2003.1238663Search in Google Scholar

[65]Śmietański, J., Tadeusiewicz, R., Łuczyńska, E.: Texture analysis in perfusion images of prostate cancer—a case study. International Journal of Applied Mathematics and Computer Science 20(1), 149–156 (2010)10.2478/v10006-010-0011-9Open DOISearch in Google Scholar

[66] Storn, R., Price, K.: Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization 11, 341–359 (1997)10.1023/A:1008202821328Search in Google Scholar

[67] Tanabe, R., Fukunaga, A.: Success-history based parameter adaptation for Differential Evolution. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 71–78. IEEE (2013)10.1109/CEC.2013.6557555Search in Google Scholar

[68] Tanabe, R., Fukunaga, A.: How far are we from an optimal, adaptive de? In: J. Handl, E. Hart, P.R. Lewis, M. López-Ibáñez, G. Ochoa, B. Paechter (eds.) Parallel Problem Solving from Nature – PPSN XIV, pp. 145–155. Springer International Publishing, Cham (2016)10.1007/978-3-319-45823-6_14Open DOISearch in Google Scholar

[69] Tanabe, R., Fukunaga, A.S.: Improving the search performance of SHADE using linear population size reduction. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 1658–1665. IEEE (2014)10.1109/CEC.2014.6900380Search in Google Scholar

[70] Tieu, K., Viola, P.: Boosting image retrieval. Int. J. Comput. Vision 56(1-2), 17–36 (2004)10.1023/B:VISI.0000004830.93820.78Search in Google Scholar

[71] Tsakiridis, N.L., Theocharis, J.B., Zalidis, G.C.: Deco3r: A differential evolution-based algorithm for generating compact fuzzy rule-based classification systems. Knowledge-Based Systems 105, 160–174 (2016)10.1016/j.knosys.2016.05.013Search in Google Scholar

[72] Tsakiridis, N.L., Theocharis, J.B., Zalidis, G.C.: Deco3rum: A differential evolution learning approach for generating compact mamdani fuzzy rule-based models. Expert Systems with Applications 83, 257–272 (2017)10.1016/j.eswa.2017.04.026Open DOISearch in Google Scholar

[73] Veltkamp, R.C., Hagedoorn, M.: State of the art in shape matching. In: M.S. Lew (ed.) Principles of Visual Information Retrieval, pp. 87–119. Springer-Verlag, London, UK, UK (2001)10.1007/978-1-4471-3702-3_4Search in Google Scholar

[74] Viktorin, A., Senkerik, R., Pluhacek, M., Kadavy, T., Zamuda, A.: Distance based parameter adaptation for differential evolution. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–7. IEEE (2017)10.1109/SSCI.2017.8280959Search in Google Scholar

[75] Viktorin, A., Senkerik, R., Pluhacek, M., Ka-davy, T., Zamuda, A.: Distance Based Parameter Adaptation for Success-History based Differential Evolution. Swarm and Evolutionary Computation (Available online 12 November 2018). DOI 10.1016/j.swevo.2018.10.01310.1016/j.swevo.2018.10.013Open DOISearch in Google Scholar

[76] Viktorin, A., Senkerik, R., Pluhacek, M., Zamuda, A.: Steady success clusters in Differential Evolution. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8. IEEE (2016)10.1109/SSCI.2016.7850252Search in Google Scholar

[77] Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, vol. 1, pp. I–511–I–518 vol.1 (2001)Search in Google Scholar

[78] Voloshynovskiy, S., Diephuis, M., Kostadinov, D., Farhadzadeh, F., Holotyak, T.: On accuracy, robustness, and security of bag-of-word search systems. In: IS&T/SPIE Electronic Imaging, pp. 902,807–902,807. International Society for Optics and Photonics (2014)10.1117/12.2038149Search in Google Scholar

[79] Wu, G., Shen, X., Li, H., Chen, H., Lin, A., Sugan-than, P.N.: Ensemble of differential evolution variants. Information Sciences 423, 172–186 (2018)10.1016/j.ins.2017.09.053Search in Google Scholar

[80] Yi, K.M., Trulls, E., Lepetit, V., Fua, P.: Lift: Learned invariant feature transform. In: European Conference on Computer Vision, pp. 467–483. Springer (2016)10.1007/978-3-319-46466-4_28Search in Google Scholar

[81] Zamuda, A., Brest, J.: Self-adaptive control parameters’ randomization frequency and propagations in differential evolution. Swarm and Evolutionary Computation 25, 72–99 (2015)10.1016/j.swevo.2015.10.007Search in Google Scholar

[82] Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: A comprehensive study. In: Computer Vision and Pattern Recognition Workshop, 2006. CVPRW ’06. Conference on, pp. 13–13 (2006). DOI 10.1109/CVPRW.2006.12110.1109/CVPRW.2006.121Open DOISearch in Google Scholar

[83] Zhang, J., Sanderson, A.C.: JADE: Adaptive Differential Evolution With Optional External Archive. IEEE Transactions on Evolutionary Computation 13(5), 945–958 (2009)10.1109/TEVC.2009.2014613Open DOISearch in Google Scholar

[84] Zhang, W., Yu, B., Zelinsky, G., Samaras, D.: Object class recognition using multiple layer boosting with heterogeneous features. In: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, vol. 2, pp. 323–330 vol. 2 (2005). DOI 10.1109/CVPR.2005.25110.1109/CVPR.2005.251Open DOISearch in Google Scholar

[85] Zitnick, C., Dollar, P.: Edge boxes: Locating object proposals from edges. In: D. Fleet, T. Pajdla, B. Schiele, T. Tuytelaars (eds.) Computer Vision – ECCV 2014, Lecture Notes in Computer10.1007/978-3-319-10602-1_26Search in Google Scholar

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