Searching for an image in a database is important different applications; hence, many algorithms have been proposed to identify the contents of image. In some applications, but not all, identifying content as whole can offer good results. object inside more most applications than whole. Therefore, studies focused on segmenting into small sub-images and identified their contents. view concepts h...

In this paper we present new probabilistic ranking functions for content based image retrieval. Our methodology generalises previous approaches and is based on the predictive densities of generative probabilistic models modelling the density of image features. We evaluate the proposed methodology and compare it against two state of the art image retrieval systems using a well known image collec...

We describe FIRE, a content-based image retrieval system, and the methods we used within this system in the ImageCLEF 2004 evaluation. In FIRE, various features are available to represent images. The diversity of available features allows the user to adapt the system to the task at hand. A weighted combination of features admits flexible query formulations and helps with processing specific que...