Relevance Feedback Based CBIR System Using SVM and Bayes Classifier

Image search techniques were not generally basedon visual features but on the textual annotation of images. Images were firstly annotated with text and then searched usinga text-based approach from traditional database management systems which is time consuming and difficult to manage. To overcome this problem, CBIR (Content Based Image Retrieval) is introduced which is becoming the hottest research area these days due to vast range of real time applications suchas Crime Prevention, Photograph Archives, Medical Diagnosis, Geographical Information and Remote Sensing System etc. The CBIR system consist of various phases to extract and matchthe features and search the images from the large scale image databases on the basis of visual contents such as Color, Shape andTexture according to the user's interest. As Semantic Gap is themost important and challenging issue. In this paper, Relevance Feedback is used to deal with this issue which based on Support Vector machine has been extensively used in the CBIR system to bridge the semantic gap between low level features and high level human perception features. The learning techniques are predominently used for the classification of images in lablelled and unlabelled datasets. In our proposed work we have to work on KNN, SVM and Bayes Classifier to classify the images. The implementation of our proposed work is done in OpenCv and experiments conducted on the Corel Dataset having 10,000 images. After attempting the experiments on various images wehave to calculate the Precision and Recall which represent in theform of graphs. After analyzing the results we have concludedthat our method is effective to reduce the semantic gap.