In this paper, a method is discussed to store and retrieve images eeciently from an image database on the basis of the data structure called E() representation. The E() representation is a spatial knowledge representation preserving the spatial information between objects embedded in symbolic images as an iconic index for the purpose of eecient image retrieval. The image retrieval method is inv...

Prior Work This Work  Large training set of densely labeled images  Weakly supervised setup: use both image tags (cheaper to obtain) and labeled pixels (expensive to obtain)  Each image classified independently  Solve jointly for all pixels and classes  Use dense image correspondences to resolve visual ambiguities Fully tagged and labeled image database Sparsely tagged and (even not) label...

Traditional content-based image retrieval technology expresses the content of each image by feature vectors. Then image retrieval process begins by calculating the similarity between the image to search and the images in the database in terms of the corresponding feature vectors, next ranks the images in the database by a descending order of similarity, and finally outputs the desired top ones....

As image databases grow large in size, index structures for fast navigation become important. In particular, when the goal is to lo cate obje ct queries in image datab ases under changes in pose, occlusions and spurious data, traditional index structures used in databases become unsuitable. This paper presents a novel index structur e called the interval hash tree, for lo cating multi-region ob...

A major research subject in image databases is to support efficient and effective access to images based on their visual content. In color image databases, this requires to support image retrieval by both global and local chromatic features. Image retrieval by color regions is complicated by the fact that regions which are requested in the query should correspond to relevant colored regions in ...

Automatic image tagging is becoming increasingly important to organize large amounts of image data. To identify concepts in images, these tagging systems rely on large sets of annotated image training sets. In this work we analyze image sets taken from online community-driven image databases, such as Flickr, for use in concept identification. Realworld performance is measured using our flexible...

The image retrieval applications are designed to fetch required images from the image databases. Images are searched using textual query or images. The textual query based retrieval is performed with image annotations. The image features are used in the content based image retrieval process on the image database provides huge collection of images. Query image features are compared with the data...

Databases have been used to record data in forensic science, such as fingerprints, shoeprints, and photos. In traditional databases, we use "text" as the "keyword" for retrieving data (text-keyword retrieval); however, in some applications, "text" is not proper to describe the searching target, and in this case, "image" plays an important role. In this paper, we use "image features" as the "key...

since increasing the number of indexing and abstracting services covering a journal would increase the total citations received by the journal, this study tried to investigate the reasons why iranian journals are not indexed by foreign databases including isi web of science and scopus. this was a descriptive and interpretative study resulting in guidelines for iranian editors and authors. it al...

This study compared patterns of oral care provided by predoctoral dental students for patients seeking treatment at the University of Washington (UW) with patterns reported for general dental offices by the Washington Dental Service (WDS). Dental care included about 5 million services provided to 880,317 patients by 2,803 WDS general dentists and about 45,600 dental services provided to 9,488 p...