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...

To bridge the semantic gap in content-based image retrieval, detecting meaningful visual entities (e.g. faces, sky, foliage, buildings etc) in image content and classifying images into semantic categories based on trained pattern classifiers have become active research trends. In this paper, we present dual cascading learning frameworks that extract and combine intraimage and inter-class semant...

One of the challenges in image and video retrieval is the content-based retrieval of images and videos in the web. Less work has been done in this area, mainly due to scalability issues. For this reason, in this paper we investigate this problem by presenting tools for the characterization of the visual contents on specific web collections and a strategy for the search of faces in the web using...

This paper presents an application of perceptual grouping rules for content-based image retrieval. The semantic interrelationships between different primitive image features are exploited by perceptual grouping to detect the presence of manmade structures. A methodology based on these principles in a Bayesian framework for the retrieval of building images, and the results obtained are presented...

The ImageCLEF Photo Retrieval Task 2009 focused on image retrieval and diversity. A new collection was utilised in this task consisting of approximately half a million images with English annotations. Queries were based on analysing search query logs and two different types were released: one containing information about image clusters; the other without. A total of 19 participants submitted 84...