Object Recognition Based on Local Steering Kernel and SVM

Abstract

The proposed method is to recognize objects based on application of Local Steering Kernels (LSK) as Descriptors to the image patches. In order to represent the local properties of the images, patch is to be extracted where the variations occur in an image. To find the interest point, Wavelet based Salient Point detector is used. Then, Local Steering Kernel is applied to the resultant pixels in order to obtain the most promising features. The features extracted will be over complete; so, in order to reduce dimensionality, Principal Component Analysis (PCA) is applied. Further, the sparse histogram is taken over the PCA output. The classifier used here is Support Vector Machine (SVM) Classifier. Bench mark database used is UIUC car database and the results obtained are satisfactory. The results obtained using LSK kernel is compared by varying parameters such as patch size, number of salient points/patches, smoothing parameter and scaling parameter.