Improved content-based classification and retrieval of images using support vector machine

Content-based image retrieval (CBIR) entails probing for similar images for a query image in an image database and returning the most relevant images. The proposed methodology aims at improving the classification and retrieval accuracy of images. Wavelet histograms are used to design a simple and efficient CBIR system with good performance and without using any intensive image-processing feature extraction technique. The unique indexed colour histogram and wavelet decomposition-based horizontal, vertical and diagonal image attributes serve as the main features for the retrieval system. Support vector machine is used for classification and thereby to improve retrieval accuracy of the system. The performance of the proposed content-based image classification and retrieval system is evaluated with the standard SIMPLIcity dataset. Precision is used as a metric to measure the performance of the system. The system is validated with holdout and k-fold cross-validation techniques. The proposed system performs better than SIMPLIcity and. all the other compared methods.