TY - JOUR ID - 72198 TI - Object Recognition based on Local Steering Kernel and SVM JO - International Journal of Engineering JA - IJE LA - en SN - 1025-2495 AU - Priyadharshini, Ramar Ahila AU - Arivazhagan, Selvaraj AD - Electronics and Communication Engineering, Mepco Schlenk Engineering College AD - Principal, Mepco Schlenk Engineering Y1 - 2013 PY - 2013 VL - 26 IS - 11 SP - 1281 EP - 1288 KW - Object Recognition KW - Salient Point Detector KW - Patch extraction KW - Local Steering Kernel KW - Principal component analysis DO - N2 - 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. Local Steering Kernel is then 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 here 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. UR - https://www.ije.ir/article_72198.html L1 - https://www.ije.ir/article_72198_eafd6381daca19a16b85e3d77aabadd8.pdf ER -