CBIR Processing Approach on Colored and Texture Images using KNN Classifier and Log-Gabor Respectively

Content Based Image Retrieval (CBIR), also called as Query By Image Content (QBIC). Content Based Image Retrieval is the method to retrieve stored image from database by supplying query image instead of text. This is achieved using proper feature extraction and matching process. Here we have implemented two methods of content based image retrieval using color and texture. In feature extraction of color is done using classifiers and similarity measure,color moment. While feature extraction of texture is done using wavelet texture features and Log-Gabor features. Finally we have retrieved top images using euclidean distance and chisquare distance and we have made comparative analysis. Content Based Image Retrieval has endless discussion to do. Here we can say that results or retrieval ratio depends upon image class for some images. we can have better precision and time complexity while some images give average result. Finally comparative analysis given in table 1 says that overall precision and time complexity given by combined approach using classifier, similarity measurev and log-gabor respectively color and texture gives better result as compared to wavelets and gabor filter. Different types of classification we can use Neural network, Support Vector Machine (SVM), KNN, Bayesian etc. In this paper, we are using K Nearest Neighbor (KNN) classifier to find out the relevant images and after that we use Spearman’s Rank Correlation Function to reduce the time complexity and improve F-measure. Hence if we want to improve retrieval efficiency we have to use some other approach. Here for effective retrieval we can use other features like shape.