Social network stores and disseminates a tremendous amount of user shared images. Deep hashing is an efficient indexing technique to support large-scale social image retrieval, due its deep representation capability, fast retrieval speed low storage cost. Particularly, unsupervised has well scalability as it does not require any manually labelled data for training. However, owing the lacking la...

The multimedia information retrieval from World Wide Web is a challenging issue. Describing multimedia object in general, images in particular with low-level features increases the semantic gap. From WWW, information present in a HTML document as textual keywords can be extracted for capturing semantic information with the view to narrow the semantic gap. The high-level textual information of i...

This paper introduces a short-term and long-term learning approach for Content-Based Image Retrieval with relevance feedback. The proposed system combines Radial Basis Function (RBF) network and the Semantic Space methods. The RBF Subsystem captures the non-linear relationship between the low-level features and the semantic meaning within an image, while the Semantic Space Subsystem stores sema...

The main problem in content–based medical image retrieval system is semantic gap, where the meaning that the user has in mind for an image is at a higher semantic level than the features on which the database operates. To overcome the shortcomings in the current content–based medical image retrieval systems and to provide a mechanism to better understand the images semantics, a fuzzy rule based...

In this article, we propose a novel system for feature selection,which is one of the key problems in contentbased image indexing and retrieval as well as various other researchfields such as pattern classification andgenomic data analysis. The proposed system aims at enhancing semantic image retrieval results, decreasing retrieval process complexity, and improving the overall system usability f...