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...

Content Based Image Retrieval (CBIR) is a set of methods for retrieving related images from an image dataset based on the image features. In the retrieval system, features recognition is the main phase of the system. One of the most important features visually recognized by humans in images is color. A company’s trademark plays an important in expansion of its business. Color is a very importan...

This work presents a novel approach to content-based image retrieval in categorical multimedia databases. The images are indexed using a combination of text and content descriptors. The categories are viewed as semantic clusters of images and are used to confine the search space.

This is to certify that I have examined this copy of a master's thesis by Anand Sivaraman and have found that it is complete and satisfactory in all respects, and that any and all revisions required by the final examining committee have been made. ABSTRACT Content-based image retrieval systems retrieve images from a database that are