Feature Vector Approximation based on Wavelet Network

Image classification is an important task in computer vision. In this paper, we propose a new image representation based on local feature vectors approximation by the wavelet networks. To extract an approximation of the feature vectors space, a Wavelet Network algorithm based on fast Wavelet is suggested. Then, the K-nearest neighbor (K-NN) classification algorithm is applied on the approximated feature vectors. The approximation of the feature space ameliorates the feature vector classification accuracy.

[1]  Chokri Ben Amar,et al.  Fast indexing method for image retrieval using tree-structured lattices , 2008, 2008 International Workshop on Content-Based Multimedia Indexing.

[2]  Chokri Ben Amar,et al.  Pyramidal Hybrid Approach: Wavelet Network with OLS Algorithm-Based Image Classification , 2011, Int. J. Wavelets Multiresolution Inf. Process..

[3]  J. Morlet,et al.  Wave propagation and sampling theory—Part I: Complex signal and scattering in multilayered media , 1982 .

[4]  Frank Nielsen,et al.  Boosting k-NN for Categorization of Natural Scenes , 2010, International Journal of Computer Vision.

[5]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[6]  James Ze Wang,et al.  SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Chokri Ben Amar,et al.  FBWN: An architecture of fast beta wavelet networks for image classification , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[8]  Charles Elkan,et al.  Using the Triangle Inequality to Accelerate k-Means , 2003, ICML.

[9]  Marjorie Skubic,et al.  Performance Evaluation of SIFT-Based Descriptors for Object Recognition , 2010 .

[10]  Xiaofeng Liao,et al.  Complex-valued wavelet network , 2003, J. Comput. Syst. Sci..

[11]  Rainer Lienhart,et al.  Comparing Local Feature Descriptors in pLSA-Based Image Models , 2008, DAGM-Symposium.

[12]  Mahmoud Mejdoub,et al.  Hierarchical categorization tree based on a combined unsupervised-supervised classification , 2011, 2011 International Conference on Innovations in Information Technology.

[13]  Frédéric Jurie,et al.  Sampling Strategies for Bag-of-Features Image Classification , 2006, ECCV.

[14]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[15]  Chokri Ben Amar,et al.  Embedded lattices tree: An efficient indexing scheme for content based retrieval on image databases , 2009, J. Vis. Commun. Image Represent..

[16]  I. Daubechies,et al.  Tree Approximation and Optimal Encoding , 2001 .

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

[18]  Chokri Ben Amar,et al.  Wavelet network for recognition system of Arabic word , 2010, Int. J. Speech Technol..

[19]  Jing Li,et al.  A comprehensive review of current local features for computer vision , 2008, Neurocomputing.

[20]  M. Moraud.  Wavelet Networks , 2018, Foundations of Wavelet Networks and Applications.

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

[22]  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).

[23]  J. van Loon.  Network , 2006 .

[24]  Chokri Ben Amar,et al.  Beta wavelets. Synthesis and application to lossy image compression , 2005, Adv. Eng. Softw..

[25]  Robert X. Gao,et al.  Base Wavelet Selection for Bearing Vibration Signal Analysis , 2009, Int. J. Wavelets Multiresolution Inf. Process..

[26]  Christine Solnon,et al.  Classification of Images Based on Hidden Markov Models , 2009, 2009 Seventh International Workshop on Content-Based Multimedia Indexing.

[27]  Andrew Zisserman,et al.  Scene Classification Using a Hybrid Generative/Discriminative Approach , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.