The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Image annotation becomes increasingly more important as the Web continues to grow. We propose a novel approach to enhancing keyword-based Web-image annotation in folksonomy, where a volunteer user is notified what kind(s) of keywords are necessary, and that keywords have been sufficiently provided by other volunteer
A large-scale image retrieval system for the WWW, named VAST (VisuAl & SemanTic image search), is presented in this paper. Based on the existing inverted file and visual feature clusters, we form a semantic network on top of the keyword association on the visual feature clusters. The system is able to
There are hundreds of millions of images available on the current World Wide Web. The demand for image retrieval online is growing dramatically. For multimedia documents, the typical keyword-based retrieval method has encountered problems mainly in the areas of: 1) the quality of the search result; 2) the usage of the
To alleviate the known semantic gap, it is necessary to integrate the two-modal parts of Web images, i.e. the low-level visual features and high-level semantic concepts (which are usually represented by keywords), for Web image retrieval. In this paper, we associate the keyword and visual features of Web images from a
The demand for image retrieval and browsing online is growing dramatically. There are hundreds of millions of images available on the current World Wide Web. For multimedia documents, the typical keyword-based retrieval methods assume that the user has an exact goal in mind in searching a set of images whereas users
and completeness through sense disambiguation and contextual meta-data prepossessing. Our schemes exploits a linguistic ontology identifying query relevant homographs used to construct sense specific keyword sets allowing for enhanced image search and result ranking via the calculation of relatedness between query
This paper proposes a new re-ranking scheme and presents experimental performance results for Web image retrieval with integrated query. In our previous work, cross-modal association rule was designed for associating one keyword with several visual feature clusters in Web image retrieval. Based on the cross-modal
. First, the related textual information associated with Web images is identified as the candidate annotations for Web images. Second, the word co-occurrence is utilized to eliminate irrelevant keywords for improving the annotation accuracy. Then, the keyword-based association analysis is exploited to further discover
As the search technology rapidly developed, nowadays, main search engines are already able to meet users basic search desire. However, current search algorithms or methodologies mostly depend on keywords matching process, which could be effective for text search while not efficient for keywords-lacking or non-text
Lack of overall ecological knowledge structure is a critical reason for learners' failure in keyword-based search. To address this issue, this paper firstly presents the dynamic location-aware and semantic hierarchy (DLASH) designed for the learners to browse images, which aims to identify learners' current
More and more abundant Web images on the Internet make clients difficult seek the information they really need so that how to quickly and accurately retrieve their interested Web images is one of the most challenging tasks. The kernel idea of the model is that the text keyword features, visual content features, link
designed and implemented to resolve the problem of crossing language queries and retrieving images processes. It can greatly reduce lot of time and effort for the search. The experiments on diverse queries on Yahoo images search have shown that the proposed scheme can improve the images results for non-English keyword
neighbor search of videos from Internet. The fundamental problem lies on the scalability of a search technique, in face of the intractable volume of videos which keep rolling on the Web. In this paper, we investigate scalability of several well-known features including color signature and visual keywords for Web-based
at the present time, the increase of e-mail spam are heavy to cumber and the spam are vastly spread. These spams cause various problems to the Internet users, such as full incoming mailbox, and wasting time. Therefore, tremendous methods have been proposed but most of them have limitation in the mapping feature and processing time. This paper proposed a method that can detect a set of image e-mail...
As a semi-structured data standard rich in both content and structure, XML is becoming the dominant information organization format in digital library. Compared with traditional information retrieval systems, XML retrieval system has great advantages in organizing and retrieving information due to its element-level rather than document-level access to relevant information. This paper gives a detailed...
The associations between different modalities of Web images could be very useful for Web image retrieval. In this paper, we investigate the multi-modal associations between two basic modalities of Web images, i.e. keyword and visual feature clusters, by data mining technique. The association rule crosses two
Through the influx of information content on the Internet, a number of image search methodologies have been presented and implemented to increase the accuracy of image retrieval including keywords, object classification and feature processing. Both keyword and object classification models rely heavily on human
In this paper we propose a new image search system using keyword annotations and low-level visual meta-data to generate inter-image relationships. Unlike other approaches the new system does not try to learn the degree of confidence between images and associated keywords. We rather propose to model the degree of
Most researches on Image Retrieval (IR) have aimed at clearing away noisy images and allowing users to search only acceptable images for a target object specified by its object-name. We have become able to get enough acceptable images of a target object just by submitting its object-name to a conventional keyword
This work aims to build a system to suggest tourist destinations based on visual matching and minimal user input. A user can provide either a photo of the desired scenary or a keyword describing the place of interest, and the system will look into its database for places that share the visual characteristics. To that
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.