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...

Content-based image retrieval and text-based image retrieval are two fundamental approaches in the field of image retrieval. The challenges related to each of these approaches, guide the researchers to use combining approaches and semi-automatic retrieval using the user interaction in the retrieval cycle. Hence, in this paper, an image retrieval system is introduced that provided two kind of qu...

one of the challenging issues in managing the existing large digital image libraries and databases is content based image retrieval (cbir). the accuracy of image retrieval methods in cbir is subject to effective extraction of image features such as color, texture, and shape. in this paper, we propose a new image retrieval method using contourlet transform coefficients to index texture of the im...

content-based image retrieval and text-based image retrieval are two fundamental approaches in the field of image retrieval. the challenges related to each of these approaches, guide the researchers to use combining approaches and semi-automatic retrieval using the user interaction in the retrieval cycle. hence, in this paper, an image retrieval system is introduced that provided two kind of qu...

In this demo proposal, we describe our prototype application, SIERRA, which combines text-based and content-based image retrieval and allows users to link together image content of varying document granularity with related data like annotations. To achieve this, we use the concept of superimposed information (SI), which enables users to (a) deal with information of varying granularity (sub-docu...

in this paper, the performance of 11 different distances for image retrieval and classification, based on color, shape and texture, is evaluated. the precision-recall measure and the correct classification rate of the k-nn classifier are used to evaluate retrieval and classification performances, respectively. the experimental results for a database of 1000 images from 10 different semantic gro...

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...

The current most desirable image retrieval feature is retrieving images based on their semantic content. Currently there are two major image retrieval paradigms that attempt to provide this: text-based metadata image retrieval and content-based image retrieval. In practical applications, both have limitations. In this paper, we discuss an ontology-based image retrieval approach that aims to sta...