Using both text and image content features, a hybrid image retrieval system for Word Wide Web is developed in this paper. We first use a text-based image metasearch engine to retrieve images from the World Wide Web based on the text information on the image host pages to provide an initial image set. Because of the high-speed and low cost nature of the text-based approach, we can easily retriev...

several semantic image search schemes have been recently proposed to retrieve images from the web. however, the query context is regularly ignored in these techniques and hence, many of the returned images are not adequately relevant. in this paper, we make use of context to further confine the outcome of the semantic search engines. for this purpose, we propose a hybrid search engine which uti...

Image Retrieval means searching, browsing, and retrieving the images from an image database. Two different methods are used for image retrieval, namely text based image retrieval and content based image retrieval techniques. But now Text based search technique is old one. In Content Based Image Retrieval many visual feature like color, shape, and texture are extracted, next when we query an ima...

Web image retrieval is a challenging task that requires efforts from image processing, link structure analysis, and web text retrieval. Since content-based image retrieval is still considered very difficult, most current large-scale web image search engines exploit text and link structure to “understand” the content of the web images. However, local text information, such as caption, filenames ...

This paper represents the first participation of the Institute of Statistical Studies and Research at Cairo University group in CLEF 2009-Medical image retrieval track. Our system uses Lemur toolkit for text retrieval. The main objective is to carry out retrieving medical image depending on associated image text. We experimented with different text features such as article title, image caption ...

In this paper, we study how to automatically exploit visual concepts in a text-based image retrieval task. First, we use Forest of Fuzzy Decision Trees (FFDTs) to automatically annotate images with visual concepts. Second, using optionally WordNet, we match visual concepts and textual query. Finally, we filter the textbased image retrieval result list using the FFDTs. This study is performed in...

Images contained in scientifi publications are widely considered useful for educational and research purposes, and their accurate indexing is critical for efficient and effective retrieval. Such image retrieval is complicated by the fact that figure in the scientifi literature often combine multiple individual subfigure (panels). Multipanel figure are in fact the predominant pattern in certain ...

One of the most significant recent advances in health information systems has been the shift from paper to electronic documents. While research on automatic text and image processing has taken separate paths, there is a growing need for joint efforts, particularly for electronic health records and biomedical literature databases. This work aims at comparing text-based versus image-based access ...

This paper describes experimental results of two approaches to multimedia image retrieval: annotation-based expansion and late fusion of mixed methods. The former formulation consists of expanding manual annotations with labels generated by automatic annotation methods. Experimental results show that the performance of text-based methods can be improved with this strategy, specially, for visual...

We introduce a variety of models, trained on a supervised image captioning corpus to predict the image features for a given caption, to perform sentence representation grounding. We train a grounded sentence encoder that achieves good performance on COCO caption and image retrieval and subsequently show that this encoder can successfully be transferred to various NLP tasks, with improved perfor...