Image Retrieval Using Hierarchical Nested Clusters

Abstract

Digital image collections are rapidly being created and made available to multitudes of users through the World Wide Web, how to retrieve the image quickly and find the desired information from the massive data becomes a big issue. And now a days, Content-based image retrieval is used for regular process of retrieving images according to image visual contents as a replacement for textual annotations. Image retrieval can be used to retrieve similar images, and the effect of image retrieval depends on the selection of image features to a certain extent.   Based on recent successes of deep learning techniques especially Convolutional Neural Networks (CNN) in solving computer vision applications, to extract more conducive to the high-level semantic feature of image retrieval using convolution neural network. Deep learning methodology combined with distance-based learning and Gaussian kernel features can be seen as recursive supervised algorithm to create new features, and hence used to provide optimal feature space for any classification method. Implementation of RSBL used in this paper is based on Euclidean distance and Gaussian kernel features.