Rapid increase of digitized document give birth to high demand of document image retrieval. While conventional document image retrieval approaches depend on complex OCR-based text recognition and text similarity detection, this paper proposes a new content-based approach, in which more attention is paid to features extraction and fusion. In the proposed approach, multiple features of document i...

Medical images are critical assets for medical diagnosis, research, and teaching. To facilitate automatic indexing and retrieval of large medical image databases, we propose a structured framework for designing and learning vocabularies of meaningful medical terms with associated visual appearance from image samples. These VisMed terms span a new feature space to represent medical image content...

The article describes the status of our ongoing R&D at the U.S. National Library of Medicine (NLM) towards the development of an advanced multimedia database biomedical information system that supports content-based image retrieval (CBIR). NLM maintains a collection of 17,000 digitized spinal X-rays along with text survey data from the Second National Health and Nutritional Examination Survey (...

A statistical correlation model for image retrieval is proposed. This model captures the semantic relationships among images in a database from simple statistics of userprovided relevance feedback information. It is applied in the post-processing of image retrieval results such that more semantically related images are returned to the user. The algorithm is easy to implement and can be efficien...

The large amount and the ubiquitous availability of multimedia information (e.g., video, audio, image, and also text documents) require e cient, e ective, and automatic annotation and retrieval methods. As videos start to play an even more important role in multimedia, content-based retrieval of videos becomes an issue, especially as there should be an integrated methodology for all types of mu...

Two new methods for retrieving mathematical expressions using conventional keyword search and expression images are presented. An expression-level TF-IDF (term frequency-inverse document frequency) approach is used for keyword search, where queries and indexed expressions are represented by keywords taken from LATEX strings. TF-IDF is computed at the level of individual expressions rather than ...

The performance of an indexing technique, namely, a system of feature extraction and similarity measure, is strongly related to the test bed in content-based image retrieval. A theoretical framework is proposed for image database modeling. The goal is to investigate the relationship between a given indexing technique and a given image database. As an analogy to the concept of perplexity of a te...

This paper presents the details of the participation of FCSE (Faculty of Computer Science and Engineering) research team in ImageCLEF 2013 medical tasks (modality classification, ad-hoc image retrieval and case-based retrieval). For the modality classification task we used SIFT descriptors and tf − idf weights of the surrounding text (image caption and paper title) as features. SVMs with χ kern...

STATISTICAL MODELS FOR TEXT QUERY-BASED IMAGE RETRIEVAL

This paper presents a joint querying and relevance feedback scheme based on both high-level and low-level features of images for an on-line content-based image retrieval system. In a high-level semantic retrieval system, we utilize the search engine to retrieve a large number of images using a given text-based query. In a low-level image retrieval process, the system provides a similar image se...