Research on application of multimedia image processing technology based on wavelet transform

With the development of information technology, multimedia has become a common information storage technology. The original information query technology has been difficult to adapt to the development of this new technology, so in order to be able to retrieve useful information in a large amount of multimedia information which has become a hot topic in the development of search technology, this paper takes the image in the multimedia information storage technology as the research object, uses the wavelet transform to divide the picture into the advantages of the low-frequency and high-frequency characteristics, and establishes the multimedia processing technology model based on the wavelet transform. The simulation results of face, vehicle, building, and landscape images show that different wavelet basis functions and different layers of images are decomposed, and the retrieval results and retrieval speed of images are different, When taking four layers of wavelet decomposition, the cubic b-spline wavelet as the wavelet basis function makes the classification result optimal, and the accuracy rate is 89.08%.

[1]  Feng Zhao,et al.  Pareto-based interval type-2 fuzzy c-means with multi-scale JND color histogram for image segmentation , 2018, Digit. Signal Process..

[2]  Ahmad Mani-Varnosfaderani,et al.  Motor Oil Classification Using Color Histograms and Pattern Recognition Techniques. , 2018, Journal of AOAC International.

[3]  Nikolas P. Galatsanos,et al.  Multichannel restoration of single channel images using a wavelet-based subband decomposition , 1994, IEEE Trans. Image Process..

[4]  Anil K. Jain,et al.  Unsupervised texture segmentation using Gabor filters , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[5]  Song Zhang,et al.  Pixel-by-pixel absolute three-dimensional shape measurement with modified Fourier transform profilometry , 2017 .

[6]  Saurabh Chaudhury,et al.  Efficient technique for rice grain classification using back-propagation neural network and wavelet decomposition , 2016, IET Comput. Vis..

[7]  Lazaros S. Iliadis,et al.  A Self-Organizing Feature Map (SOFM) model based on aggregate-ordering of local color vectors according to block similarity measures , 2013, Neurocomputing.

[8]  Jan Flusser,et al.  Pattern recognition by affine moment invariants , 1993, Pattern Recognit..

[9]  Danica Petrovic,et al.  Gray level co-occurrence matrix algorithm as pattern recognition biosensor for oxidopamine-induced changes in lymphocyte chromatin architecture. , 2016, Journal of theoretical biology.

[10]  King Ngi Ngan,et al.  Face segmentation using skin-color map in videophone applications , 1999, IEEE Trans. Circuits Syst. Video Technol..

[11]  P.G.J. Barten,et al.  Effects of quantization and pixel structure on the image quality of color matrix displays , 1991, Conference Record of the 1991 International Display Research Conference.

[12]  Igor Pantic,et al.  Fractal analysis and Gray level co-occurrence matrix method for evaluation of reperfusion injury in kidney medulla. , 2016, Journal of theoretical biology.

[13]  José Manuel Bravo,et al.  A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features , 2011, IEEE Transactions on Medical Imaging.

[14]  Nicolai Petkov,et al.  Comparison of texture features based on Gabor filters , 2002, IEEE Trans. Image Process..

[15]  Fang Cui,et al.  Short-term wind speed forecasting using the wavelet decomposition and AdaBoost technique in wind farm of East China , 2016 .

[16]  Kai Liu,et al.  An improved TLD with Harris corner and color moment , 2017, International Conference on Graphic and Image Processing.

[17]  Nicolai Petkov,et al.  Comparison of texture features based on Gabor filters , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[18]  Yun-Hui Liu,et al.  Fourier-Based Shape Servoing: A New Feedback Method to Actively Deform Soft Objects into Desired 2-D Image Contours , 2018, IEEE Transactions on Robotics.

[19]  B. S. Manjunath,et al.  Multisensor Image Fusion Using the Wavelet Transform , 1995, CVGIP Graph. Model. Image Process..

[20]  Yong Guo,et al.  Blind image watermarking method based on linear canonical wavelet transform and QR decomposition , 2016, IET Image Process..

[21]  Vandana Vinayak,et al.  CBIR System using Color Moment and Color Auto-Correlogram with Block Truncation Coding , 2017 .

[22]  K. Jain,et al.  Unsupervised Texture Segmentation Using Gabor Filters1 , 1990 .

[23]  Boualem Boashash,et al.  A human identification technique using images of the iris and wavelet transform , 1998, IEEE Trans. Signal Process..

[24]  C.-C. Jay Kuo,et al.  Texture analysis and classification with tree-structured wavelet transform , 1993, IEEE Trans. Image Process..