A Quaternionic Wavelet Transform-based Approach for Object Recognition

  • R. Ahila Priyadharshini Mepco Schlenk Engineering College, Sivakasi-626 005
  • S. Arivazhagan Mepco Schlenk Engineering College, Sivakasi-626 005
Keywords: Object recognition, salient point detector, patch, quaternionic wavelet transform


Recognizing the objects in complex natural scenes is the challenging task as the object may be occluded, may vary in shape, position and in size. In this paper a method to recognize objects from different categories of images using quaternionic wavelet transform (QWT) is presented. This transform separates the information contained in the image better than a traditional Discrete wavelet transform and provides a multiscale image analysis whose coefficients are 2D analytic, with one near-shift invariant magnitude and three phases. The two phases encode local image shifts and the third one contains texture information. In the domain of object recognition, it is often to classify objects from images that make only limited part of the image. Hence to identify local features and certain region of images, patches are extracted over the interest points detected from the original image using Wavelet based interest point detector. Here QWT magnitude and phase features are computed for every patch. Then these features are trained, tested and classified using SVM classifier in order to have supervised learning model. In order to compare the performance of local feature with global feature, the transform is applied to the entire image and the global features are derived. The performance of QWT is compared with discrete wavelet transform (DWT) and dual tree discrete wavelet transform (DTDWT). Observations revealed that QWT outperforms the DWT and shift invariant DTDWT with lesser equal error rate. The experimental evaluation is done using the complex Graz databases.

Defence Science Journal, Vol. 64, No. 4, July 2014, pp. 350-357, DOI:http://dx.doi.org/10.14429/dsj.64.4503

Author Biographies

R. Ahila Priyadharshini, Mepco Schlenk Engineering College, Sivakasi-626 005
Mrs R. Ahila Priyadharshini obtained her ME (Communication Systems) from Madurai Kamaraj University, in 2002. She is currently working as Assistant Professor in the Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi. She has published nearly 18 technical papers in International Journals/Conferences. Her current research interests include: Pattern recognition and image processing.
S. Arivazhagan, Mepco Schlenk Engineering College, Sivakasi-626 005
Dr S. Arivazhagan obtained PhD from Manonmaniam Sundaranar University, Tirunelveli, in 2005. Presently, he is working as Principal, MepcoSchlenk Engineering College, Sivakasi. He has published and presented more than 142 Technical papers in the International /National Journals and Conferences. Presently he is Governing Council member of IETE, New Delhi. His current research interests include : Image processing, pattern recognition and computer communication.
How to Cite
Priyadharshini, R., & Arivazhagan, S. (2014). A Quaternionic Wavelet Transform-based Approach for Object Recognition. Defence Science Journal, 64(4), 350-357. https://doi.org/10.14429/dsj.64.4503
Electronics & Communication Systems