Keywords: Object recognition, salient point detector, patch, quaternionic wavelet transform The experimental evaluation is done using the complex Graz databases. Observations revealed that QWT outperforms the DWT and shift invariant DTDWT with lesser equal error rate. The performance of QWT is compared with discrete wavelet transform (DWT) and dual tree discrete wavelet transform (DTDWT). 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. Then these features are trained, tested and classified using SVM classifier in order to have supervised learning model. Here QWT magnitude and phase features are computed for every patch. 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. In the domain of object recognition, it is often to classify objects from images that make only limited part of the image. The two phases encode local image shifts and the third one contains texture information. 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. In this paper a method to recognize objects from different categories of images using quaternionic wavelet transform (QWT) is presented. * Mepco Schlenk Engineering College, Sivakasi-626 005, India *E-mail: the objects in complex natural scenes is the challenging task as the object may be occluded, may vary in shape, position and in size.

Received 18 April 2013, revised 20 April 2014, online published 21 July 2014Ī Quaternionic Wavelet Transform-based Approach for Object Recognition

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