A Novel Method for CBIR Using SVM Classifier with Multi Features

J. Binisha Rose and N. Santhi

Content based Remote Sensing Image Retrieval (CBRSIR) systems are used to retrieve useful contents from a massive amount of remote sensing images. A proposed novel technique for remote sensing image retrieval using SVM (Support Vector Machine) classifier with multi-features such as HOG (Histogram of Oriented Gradients), Color moment, Gabor and wavelet. Initially, the color feature is extracted from the satellite image using the peculiar color moments and are invariant to scaling and rotation. Texture features are extracted using dominant HOG, Gabor and Wavelet then the feature selection methods are separately classified. In the existing system, the images are first retrieved and then classified. Moreover the retrieval rates obtained by the existing techniques are not satisfactory. Hence a method is proposed as a novel multi feature based SVM technique where it initially classifies the different class from the data base and get the similar retrieval images based upon the feature of the query image. Thus by finding the retrieval rate after performing the classification, it is evident that output retrieval rate is better by comparing with other models. The fundamental performance metrics like accuracy, sensitivity and specificity are taken into comparison. The proposed method has higher accuracy when it is compared to the accuracy of other feature based SVM.

Volume 11 | Issue 7

Pages: 340-348