The Bag-of-Features Algorithm for Practical Applications Using the MySQL Database

This article presents a modification of the Bag-of-Features method (also known as a Bag-of-Words or Bag-of-Visual-Words method) used for image recognition in practical applications using a relational database. Our approach utilises a modified k-means algorithm, owing to which the number of clusters is automatically selected, and also the majority votes method when making decisions in the classification process. The algorithm can be used both methods in an SQL Server database or a commonly-used MySQL one. The proposed approach minimises the necessity to use additional algorithms and/or classifiers in the image classification process. This makes it possible to significantly simplify computations and use the SQL language.

[1]  Bernd Fritzke.  Growing Grid — a self-organizing network with constant neighborhood range and adaptation strength , 1995, Neural Processing Letters.

[2]  Marcin Gabryel,et al.  A finite-buffer queue with a single vacation policy: An analytical study with evolutionary positioning , 2014, Int. J. Appl. Math. Comput. Sci..

[3]  Ahmad Fouad El-Samak,et al.  Optimization of Traveling Salesman Problem Using Affinity Propagation Clustering and Genetic Algorithm , 2015, J. Artif. Intell. Soft Comput. Res..

[4]  Piotr Duda,et al.  Decision Trees for Mining Data Streams Based on the McDiarmid's Bound , 2013, IEEE Transactions on Knowledge and Data Engineering.

[5]  Marcin Korytkowski,et al.  Neuro-fuzzy Rough Classifier Ensemble , 2009, ICANN.

[6]  Robert Nowicki.  Rough Sets in the Neuro-Fuzzy Architectures Based on Monotonic Fuzzy Implications , 2004, ICAISC.

[7]  Jialu Liu,et al.  Image Retrieval based on Bag-of-Words model , 2013, ArXiv.

[8]  Piotr Duda,et al.  The CART decision tree for mining data streams , 2014, Inf. Sci..

[9]  Bin Xiao,et al.  Object recognition based on the Region of Interest and optimal Bag of Words model , 2016, Neurocomputing.

[10]  Shigeaki Sakurai,et al.  A New Approach For Discovering Top-K Sequential Patterns Based On The Variety Of Items , 2015, J. Artif. Intell. Soft Comput. Res..

[11]  Jian Sun,et al.  Image classification with Bag-of-Words model based on improved SIFT algorithm , 2013, 2013 9th Asian Control Conference (ASCC).

[12]  Robert Nowicki,et al.  On design of flexible neuro-fuzzy systems for nonlinear modelling , 2013, Int. J. Gen. Syst..

[13]  Rafal Grycuk,et al.  From Single Image to List of Objects Based on Edge and Blob Detection , 2014, ICAISC.

[14]  Pietro Perona,et al.  Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[15]  Piotr Duda,et al.  Decision Trees for Mining Data Streams Based on the Gaussian Approximation , 2014, IEEE Transactions on Knowledge and Data Engineering.

[16]  Janusz T. Starczewski.  Centroid of triangular and Gaussian type-2 fuzzy sets , 2014, Inf. Sci..

[17]  Ricardo Tanscheit,et al.  GPFIS-Control: A Genetic Fuzzy System For Control Tasks , 2014, J. Artif. Intell. Soft Comput. Res..

[18]  Marcin Korytkowski,et al.  Application of Neural Networks in Assessing Changes around Implant after Total Hip Arthroplasty , 2012, ICAISC.

[19]  Krystian Lapa,et al.  A New Method for Designing and Complexity Reduction of Neuro-fuzzy Systems for Nonlinear Modelling , 2013, ICAISC.

[20]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[21]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[22]  Chunhui Zhao,et al.  Bisecting k-means clustering based face recognition using block-based bag of words model , 2015 .

[23]  Rafal Grycuk,et al.  Image Indexing and Retrieval Using GSOM Algorithm , 2015, ICAISC.

[24]  Marcin Korytkowski,et al.  Forecasting Wear of Head and Acetabulum in Hip Joint Implant , 2012, ICAISC.

[25]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[26]  Marcin Korytkowski,et al.  Fast image classification by boosting fuzzy classifiers , 2016, Inf. Sci..

[27]  Mehdi Hosseinzadeh Aghdam,et al.  Feature Selection Using Particle Swarm Optimization in Text Categorization , 2015, J. Artif. Intell. Soft Comput. Res..

[28]  Loris Nanni,et al.  Combination of projectors, standard texture descriptors and bag of features for classifying images , 2016, Neurocomputing.

[29]  Simone A. Ludwig,et al.  Particle Swarm Optimization Based Fuzzy Clustering Approach to Identify Optimal Number of Clusters , 2014, J. Artif. Intell. Soft Comput. Res..

[30]  Janusz T. Starczewski,et al.  The Learning of Neuro-Fuzzy Classifier with Fuzzy Rough Sets for Imprecise Datasets , 2014, ICAISC.

[31]  Piotr Duda,et al.  A New Method for Data Stream Mining Based on the Misclassification Error , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[32]  Miltiadis Chalikias,et al.  Maximising Accuracy and Efficiency of Traffic Accident Prediction Combining Information Mining with Computational Intelligence Approaches and Decision Trees , 2014, J. Artif. Intell. Soft Comput. Res..

[33]  Marcin Gabryel,et al.  Can We Process 2D Images Using Artificial Bee Colony? , 2015, ICAISC.

[34]  Krzysztof Cpalka,et al.  A New Method for Design and Reduction of Neuro-Fuzzy Classification Systems , 2009, IEEE Transactions on Neural Networks.