SVM CLASSIFIER BASED OBJECT RECOGNITION

• 1, Department of ECE, Regional Office of Anna University, Madurai, Tamil Nadu, India, IN

Object Recognition Plays an important role in analyzing the contents of image and video. This paper is to find the tag of the input image based on the training image and perform the image retrieval by the generating tag. To tag the image first find which content found on that image. To do this work the training images is needed because this project makes comparison between the input image and the input image to find the similarities. Then put the tag based on the most similar images. The feature extraction technique is used to find the content of the image. The DWT, Gabor filter, Edge histogram descriptor, Color moment and SIFT are used for feature extraction. To find the similarities between the training and the testing images feature extraction is used. After extracting the features one classifier is needed. SVM is used in this project and this classifier finds the most similar image from the training image then the tag of the similar image is considered as the final tag. Then this final tag is applied on the input image.