Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic

Open access

Abstract

Fast content-based image retrieval is still a challenge for computer systems. We present a novel method aimed at classifying images by fuzzy rules and local image features. The fuzzy rule base is generated in the first stage by a boosting procedure. Boosting meta-learning is used to find the most representative local features. We briefly explore the utilization of metaheuristic algorithms for the various tasks of fuzzy systems optimization. We also provide a comprehensive description of the current best-performing DISH algorithm, which represents a powerful version of the differential evolution algorithm with effective embedded mechanisms for stronger exploration and preservation of the population diversity, designed for higher dimensional and complex optimization tasks. The algorithm is used to fine-tune the fuzzy rule base. The fuzzy rules can also be used to create a database index to retrieve images similar to the query image fast. The proposed approach is tested on a state-of-the-art image dataset and compared with the bag-of-features image representation model combined with the Support Vector Machine classification. The novel method gives a better classification accuracy, and the time of the training and testing process is significantly shorter.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • [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)

  • [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 Interaction

  • [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)

  • [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)

  • [5] Bay H. Ess A. Tuytelaars T. Van Gool L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3) 346–359 (2008)

  • [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)

  • [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)

  • [8] Bradski G.: The opencv library. Doctor Dobbs Journal 25(11) 120–126 (2000)

  • [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] 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)

  • [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)

  • [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)

  • [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)

  • [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 10.1109/83.242353

  • [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)

  • [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)

  • [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)

  • [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)

  • [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)

  • [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)

  • [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)

  • [22] Fernández A. Herrera F.: Evolutionary Fuzzy Systems: A Case Study in Imbalanced Classification pp. 169–200. Springer International Publishing Cham (2016)

  • [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 10.1109/78.229897

  • [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)

  • [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)

  • [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)

  • [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.2005.138

  • [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)

  • [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.1997.609412

  • [30] Jagadish H.V.: A retrieval technique for similar shapes. SIGMOD Rec. 20(2) 208–217 (1991)

  • [31] Jain A.K. Farrokhnia F.: Unsupervised texture segmentation using gabor filters. Pattern Recognition 24(12) 1167 – 1186 (1991)

  • [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 10.1109/34.368168

  • [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)

  • [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.2013.6557971

  • [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.2017.05.195

  • [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)

  • [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.2015.2510365

  • [38] Lowe D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2) 91–110 (2004)

  • [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)

  • [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)

  • [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 2002

  • [42] Mikolajczyk K. Schmid C.: Scale and affine invariant interest point detectors. International Journal of Computer Vision 60(1) 63–86 (2004)

  • [43] Mininno E. Neri F. Cupertino F. Naso D.: Compact Differential Evolution. IEEE Transactions on Evolutionary Computation 15(1) 32–54 (2011)

  • [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)

  • [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)

  • [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.2018.06.010

  • [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.1996.572008

  • [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)

  • [49] Piotrowski A.P.: aL-SHADE optimization algorithms with population-wide inertia. Information Sciences (2018)

  • [50] Piotrowski A.P. Napiorkowski J.J.: Some meta-heuristics should be simplified. Information Sciences 427 32–62 (2018)

  • [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.2018.03.007

  • [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/1808.08517

  • [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)

  • [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)

  • [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)

  • [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)

  • [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.2011.6126544

  • [58] Rudziński F.: A multi-objective genetic optimization of interpretability-oriented fuzzy rule-based classifiers. Applied Soft Computing 38 118 – 133 (2016)

  • [59] Rutkowski L.: Computational Intelligence Methods and Techniques. Springer Berlin Heidelberg (2008)

  • [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)

  • [61] Scherer R.: Designing boosting ensemble of relational fuzzy systems. International Journal of Neural Systems 20(5) 381–388 (2010)

  • [62] Scherer R.: Multiple Fuzzy Classification Systems. Springer Publishing Company Incorporated (2014)

  • [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)

  • [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)

  • [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)

  • [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)

  • [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)

  • [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)

  • [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)

  • [70] Tieu K. Viola P.: Boosting image retrieval. Int. J. Comput. Vision 56(1-2) 17–36 (2004)

  • [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)

  • [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)

  • [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)

  • [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)

  • [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.2018.10.013

  • [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)

  • [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)

  • [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. 902807–902807. International Society for Optics and Photonics (2014)

  • [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)

  • [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)

  • [81] Zamuda A. Brest J.: Self-adaptive control parameters’ randomization frequency and propagations in differential evolution. Swarm and Evolutionary Computation 25 72–99 (2015)

  • [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.2006.121

  • [83] Zhang J. Sanderson A.C.: JADE: Adaptive Differential Evolution With Optional External Archive. IEEE Transactions on Evolutionary Computation 13(5) 945–958 (2009)

  • [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.2005.251

  • [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 Computer

Search
Journal information
Impact Factor


CiteScore 2018: 4.70

SCImago Journal Rank (SJR) 2018: 0.351
Source Normalized Impact per Paper (SNIP) 2018: 4.066

Metrics
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 71 71 53
PDF Downloads 81 81 59