Choosing the Method of Finding Similar Images in the Reverse Search System

The article describes the research of image analysis methods. The methods of indexing images for the search of duplicate images, as well as methods for finding similar images based on the definition of key points are described. The prototype of the system was created, and testing of the described methods was carried out.

[1]  Oleksiy Lutsyk,et al.  Lossless image compression in the remote sensing applications , 2016, 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP).

[2]  Victoria Vysotska,et al.  Process analysis in electronic content commerce system , 2015, 2015 Xth International Scientific and Technical Conference "Computer Sciences and Information Technologies" (CSIT).

[3]  Adrien Bartoli,et al.  Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces , 2013, BMVC.

[4]  Ivan Izonin,et al.  Learning-Based Image Scaling Using Neural-Like Structure of Geometric Transformation Paradigm , 2018 .

[5]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[6]  Yevhen Burov,et al.  Information resources processing using linguistic analysis of textual content , 2017, 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS).

[7]  Vasyl Lytvyn,et al.  Time Dependence of the Output Signal Morphology for Nonlinear Oscillator Neuron Based on Van der Pol Model , 2018 .

[8]  Vincent Lepetit,et al.  BRIEF: Binary Robust Independent Elementary Features , 2010, ECCV.

[9]  Natalia Lotoshynska,et al.  Single-frame image super-resolution based on singular square matrix operator , 2017, 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON).

[10]  Joachim Weickert,et al.  Cyclic Schemes for PDE-Based Image Analysis , 2016, International Journal of Computer Vision.

[11]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[12]  Ivan Izonin,et al.  Image Superresolution via Divergence Matrix and Automatic Detection of Crossover , 2016 .

[13]  Vasyl Teslyuk,et al.  Development and Implementation of the Technical Accident Prevention Subsystem for the Smart Home System , 2018 .

[14]  Xin Yang,et al.  LDB: An ultra-fast feature for scalable Augmented Reality on mobile devices , 2012, 2012 IEEE International Symposium on Mixed and Augmented Reality (ISMAR).

[15]  Vasyl Lytvyn,et al.  Smart Data Integration by Goal Driven Ontology Learning , 2016, INNS Conference on Big Data.

[16]  Roland Siegwart,et al.  BRISK: Binary Robust invariant scalable keypoints , 2011, 2011 International Conference on Computer Vision.

[17]  Vasyl Lytvyn,et al.  The risk management modelling in multi project environment , 2017, 2017 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT).

[18]  L. Chyrun,et al.  Analysis features of information resources processing , 2015, 2015 Xth International Scientific and Technical Conference "Computer Sciences and Information Technologies" (CSIT).