ABTRACT

In this thesis performance a classifier i.e. Support machine (SVM) linear kernel mapping is analyzed. various classes of images of Dataset PASCAL VOC 2007 are used for classification. The dataset of consists of different classes whose features are extracted by using Scale Invariant Feature transform (SIFT) and quantized into visual words. Then he frequency of vectors is recorded in a histogram for each spatial tile of the image. Then the resulting vectors (features are used to train the (SVM) for different class of images and evaluate the performance of the classifier. The analyzing results give us the performance accuracy of different classes of images with different representation and performance of the SVM classifier is measured by using different parameters such ascomputation Time: Accuracy. Precision and Recall curveand Average Precision.
SVM is powerful classification algorithm alternative to neural network. A.s compare to the neural network SVM classifier gives high generalization performance when the measurement of the input pace is very high and no need to add a prior knowledge.

It is observed from theparameters PR Curve and AP value that the performance of the classifier is increased a it learn m re and m r positive images given are contained in the dataset.

Keywords: feature Extraction. Image Classification. Linear kernel. SIIFT Support Vector machine.