Content-based image retrieval optimization by differential evolution

In this paper we present a new method for content-based searching large image databases by comparing content of a query image and images stored in a database. The algorithm consists of three main steps: feature extraction, indexing and system learning. The feature extraction stage is based on two types of features (SURF keypoints and color). For indexing we use the k-means algorithm and for system learning we applied differential evolution. This last step is very important, and significantly improves the results. The presented algorithm can be easily modified, by changing its components (feature extractor or clustering algorithm).

[1]  Matthieu Cord,et al.  Advanced Techniques in CBIR: Local Descriptors, Visual Dictionaries and Bags of Features , 2009, 2009 Tutorials of the XXII Brazilian Symposium on Computer Graphics and Image Processing.

[2]  Libor Preucil,et al.  FPGA based Speeded Up Robust Features , 2009, 2009 IEEE International Conference on Technologies for Practical Robot Applications.

[3]  C. Hung,et al.  A Comparative Study on Differential Evolution and Genetic Algorithms for Some Combinatorial Problems , 2009 .

[4]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[5]  김은이,et al.  Mean Shift Clustering을 이용한 영상 검색결과 개선 , 2009 .

[6]  Paul L. Rosin,et al.  Incorporating shape into histograms for CBIR , 2002, Pattern Recognit..

[7]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[8]  Francesco G. B. De Natale,et al.  Evolutionary image retrieval , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

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

[10]  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..

[11]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[12]  Guillaume Lavoué,et al.  Combination of bag-of-words descriptors for robust partial shape retrieval , 2012, The Visual Computer.

[13]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[14]  Muhammad Riaz,et al.  CBIR Based on Adaptive Segmentation of HSV Color Space , 2010, 2010 12th International Conference on Computer Modelling and Simulation.

[15]  Eren Bas,et al.  The Training Of Multiplicative Neuron Model Based Artificial Neural Networks With Differential Evolution Algorithm For Forecasting , 2016, J. Artif. Intell. Soft Comput. Res..

[16]  S Neetu Sharma,et al.  Efficient Cbir Using Color Histogram Processing , 2012 .

[17]  Ling Guan,et al.  An interactive approach for CBIR using a network of radial basis functions , 2004, IEEE Transactions on Multimedia.

[18]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[19]  Yizong Cheng,et al.  Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

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

[21]  Marcin Gabryel,et al.  Evolutionary Methods for Designing Neuro-fuzzy Modular Systems Combined by Bagging Algorithm , 2008, ICAISC.

[22]  Rafal Grycuk,et al.  Multi-layer Architecture For Storing Visual Data Based on WCF and Microsoft SQL Server Database , 2015, ICAISC.

[23]  Alexandre X. Falcão,et al.  A new CBIR approach based on relevance feedback and optimum-path forest classification , 2010, J. WSCG.

[24]  M. Venkat Dass,et al.  Image Retrieval Using Interactive Genetic Algorithm , 2014, 2014 International Conference on Computational Science and Computational Intelligence.

[25]  Timothy B. Terriberry,et al.  GPU Accelerating Speeded-Up Robust Features , 2008 .

[26]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[27]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[28]  Guillaume Lavoué,et al.  Bag of Words and Local Spectral Descriptor for 3D Partial Shape Retrieval , 2011, 3DOR@Eurographics.

[29]  De Xu,et al.  Attention-driven salient edge(s) and region(s) extraction with application to CBIR , 2010, Signal Process..

[30]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[31]  Lei Zhang,et al.  A CBIR method based on color-spatial feature , 1999, Proceedings of IEEE. IEEE Region 10 Conference. TENCON 99. 'Multimedia Technology for Asia-Pacific Information Infrastructure' (Cat. No.99CH37030).

[32]  Rafal Grycuk,et al.  Improved Digital Image Segmentation Based on Stereo Vision and Mean Shift Algorithm , 2013, PPAM.

[33]  Piraveenan Mahendra,et al.  Evolutionary Stable Strategies In Networked Games: The Influence Of Topology , 2015, J. Artif. Intell. Soft Comput. Res..

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

[35]  Marcin Gabryel,et al.  New image descriptor from edge detector and blob extractor , 2015 .

[36]  Samia Boucherkha,et al.  Color quantization and its impact on color histogram based image retrieval accuracy , 2009, 2009 First International Conference on Networked Digital Technologies.

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

[38]  Rafal Grycuk,et al.  Content-Based Image Indexing by Data Clustering and Inverse Document Frequency , 2014, BDAS.

[39]  Francesc J. Ferri,et al.  An interactive evolutionary approach for content based image retrieval , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[40]  Pierre Vandergheynst,et al.  FREAK: Fast Retina Keypoint , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[41]  Marcin Gabryel,et al.  On Applying Evolutionary Computation Methods to Optimization of Vacation Cycle Costs in Finite-Buffer Queue , 2014, ICAISC.

[42]  René Thomsen,et al.  A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[43]  Shengjiu Wang.  A Robust CBIR Approach Using Local Color Histograms , 2001 .