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keywords from the Web pages. The system first identifies the section of the Web page that contains the multimedia file to be extracted and then extracts it by using clustering techniques and other tools of statistical origin. Experimental results on real-world image sharing Web sites are presented and discussed in this paper
The traditional layout of news websites, the combination of classified hierarchical browsing, headline recommendation and keyword-based search, has been used for many years. The keyword-based search is considered to be the most powerful tool for news browsing and retrieval. Unfortunately, the keyword-based query
be easily extracted, building respective data banks. Keywords are the important terms, sometimes called, index terms that contain some kind of valuable information about the document. Automatic keyword extraction is the task to identify a small set of words, which can be designated as keywords for that document, and
This paper describes experiments for audio clips comparison based on spoken context. The spoken content is obtained using automatic speech recognition. The social tags that are available for most of the audio clips are used as keywords. These keywords are mapped to the spoken transcription representing the audio clips
associated with an image. In our approach, we divide images into small tiles and create visual keywords using a high-dimensional clustering algorithm. These visual keywords act the same as text keywords. One of the challenges of this approach is to identify an appropriate size for visual keywords. In this paper, we report our
vector of the wood image. This keyblock distribution based wood image retrieval algorithm is similar to the keyword based text retrieval algorithm. In the text retrieval algorithm, keyword can be used to represent the content of the text. Similarly, the keyblock in our proposed algorithm can be used to represent the content
We investigate a subgraph mining framework, that can connect similar entities according to their structure and attribute similarities. We take one mapping between two related points chosen from the query and target graph as one vertex in the correspondence graph and decide the weight of the edge based on the similarity score. In this way, we transform the problem to a dense subgraph discovery problem...
videos, we can only use a title. If there are tags - significant keywords of that multimedia, we can use tag information to search. Tag is a keyword of text, blog post, or multimedia. Users have already recognized about the value and importance of tags but only a few users are using tags. They might be annoying to add tags
currently produce effective annotations for retrieval, while manual annotation is expensive. The proposed approach uses low-level feature similarity to guide the retrieval of keyword annotations and aims to preserve the high quality of manual annotations while reducing the time and cost per annotated video unit. The annotation
In this paper, we propose a multimodal query suggestion method for video search engine which can leverage multimodal processing to improve the quality of search results. When users type general or ambiguous textual queries, our system provides keyword suggestions and representative image examples in an easy-to-use
search techniques. In this paper, we introduce an associated semantic network as the semantic representation model; use semantic keywords, a linguistic ontology in semantic similarity calculation and use learner relevance feedback to complete automatic semantic annotation. After several iterations of learner relevance
the age of Big Data where Velocity, Variety and Volume are the challenges, variety of data that include structured as well as unstructured data is the most important issue. Image mining in Big Data is the challenge need to be addressed, so the proposed work compose an image query object, aspect of use, Keywords to
. Current approaches for searching in big multimedia collections mainly rely on keywords. However, manually annotating every single object in a large collection is not feasible. Therefore, content-based multimedia retrieval -using sample objects as query input - is increasingly becoming an important requirement for dealing
into a summary. WebSum is an enhancement of the SumBasic algorithm, that was mainly used for multi-document summarization. In the case of Web sites, we find that several Web characteristics such as title and keywords can be used to extract sentences that may represent the overall topic of the Web site. Initial results
This work identifies relevant songs from a user's personal music collection to accompany pictures of an event. The event's pictures are analyzed to extract aggregated semantic concepts in a variety of dimensions, including scene type, geospatial information, and event type, along with user-provided keywords. These
relations and bi relation patterns; TCMW-MODEL is used for acquiring the sets of the domain keywords in the traditional Chinese medicine. Our experimental results show the precision/recall of data extraction using this system is as good as those from the templates based on structured information extraction. The domain experts
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