Extraction and recognition of text from image is an important step in building efficient indexing and retrieval systems for multimedia databases. Our primary objective is to make an unconstrained image indexing and retrieval system using neural network. We adopt HSV based approaches for color reduction. This approach show impressive results. We extract a set of features from each ROI for that s...

Biomedical images are invaluable in establishing diagnosis, acquiring technical skills, and implementing best practices in many areas of medicine. At present, images needed for instructional purposes or in support of clinical decisions appear in specialized databases and in biomedical articles, and are often not easily accessible to retrieval tools. Our goal is to automatically annotate images ...

Content based image retrieval is the task of retrieve the images from the large collection of database on the basis of their own visual content. This paper provides the survey of technical achievements in the research area of image retrieval, especially content based image retrieval (CBIR. Color and texture are commonly used in most of the CBIR system for finding similar images from the databas...

Most traditional image retrieval approaches are based on text surrounding images and fail to capture the content information of the images. These approaches are able to satisfy the present demands. To overcome the limitations of previous approaches, in this paper, we propose a new image retrieval method based on probability similarity learning which is able to exploit probabilistic distribution...

We present a statistical model for organizing image collections which integrates semantic information provided by associated text and visual information provided by image features. The model is very promising for information retrieval tasks such as database browsing and searching for images based on text and/or image features. Furthermore, since the model learns relationships between text and i...

The aim of this document is to describe our methods used in the Medical Image Modality Classification and Ad-hoc Image Retrieval Tasks of ImageClef 2011. The main novelty in medical image modality classification this year was, that there were more classes (18 modalities) organized in a hierarchy and for some categories only few annotated examples were available. Therefore, our strategy in image...

In Information Retrieval (IR), the semantic gap is the difference between what computers store and what users expect via their queries. There are several reasons for the existence of those gaps such as homonymy and synonymy in text retrieval, or the typical difference between low-level representations and keyword-based queries in image retrieval. The objective of this work is to close these gap...

In this age of computers, virtually all spheres of human life including commerce, government, academics, hospitals, crime prevention, surveillance, engineering, architecture, journalism, fashion and graphic design, and historical research use images for efficient services. In the medical profession, X-rays and scanned image database are kept for diagnosis, monitoring, and research purposes. In ...

In these days an image retrieval system has become a challenging task. Many systems based on the text based retrieval but the need of image based retrieval system that takes an image as the input query and retrieves images based on image content is more complicated task. Content Based Image Retrieval is an approach for retrieving semanticallyrelevant images from an image database based on autom...

In the commercial use of picture collections, a heavy dependency continues to be exhibited on a concept-based image retrieval paradigm in which the query is verbalised by the client and resolved as a metadata text-matching operation. The practical and philosophical challenges posed by the indexing aspect of image metadata construction are significant and frequently expressed. Nevertheless, it h...