image retrieval is an important research field which has received great attention in the last decades. in this paper, we present an approach for the image retrieval based on the combination of text-based and content-based features. for text-based features, keywords and for content-based features, color and texture features have been used. query in this system contains some keywords and an input...

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...

Automatic extraction of information from music data is an important and challenging issue in the field of content-based music retrieval. As part of the research effort, this study presents a technique that automatically identifies cover versions of songs specified by users. The technique enables users to search for songs with an identical tune, but performed by different singers, in different l...