In the paper an image database system (named SOFIA) with a powerful image retrieval by image features and image content is described. A set of novel image indexing mechanism is proposed. A query by colour, shape, image description and image element description is developed. The SOFIA system is been implemented on IBM PC using Borland C++ under Windows 3.1.

In this paper, we present a new image indexing and retrieval method for image database applications. We introduce a novel image content description feature termed Scene Structural Matrix (SSM). The SSM captures the overall structural information of the scene by indexing the geometric features of the image. In this work, we also introduce a constraint adaptive image segmentation method termed Mi...

For e cient search and spatial similarity-based retrieval of image contents, this paper introduces a new symbolic image representation and indexing technique. In this technique, an image is recursively decomposed into a spatial arrangement of features points while preserving the spatial relationships among its various component. Quadtrees are used to manage the decomposition hierarchy and help ...

One of the main challenges in content-based image retrieval still remains to bridge the gap between low-level features and semantic information. In this paper, we present our first results concerning a medical image retrieval approach using a semantic medical image and report indexing within a fusion framework, based on the Unified Medical Language System (UMLS) metathesaurus. We propose a stru...

Purpose – User-created metadata, often referred to as folksonomy or social classification, has received a considerable amount of attention in the digital library world. Social tagging is perceived as a tool for enhancing description of digital objects and providing a venue for user input and greater user engagement. This article seeks to examine the pros and cons of user-generated metadata in t...