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Users usually search images in different web image search engines by inputting a specified keyword. However, the true images, drawing images, sometimes contemporary images exist in search results. To solve this problem, this paper proposed a simple color feature by using the histogram method for building image
As the popularity of text-based source code analysis grows, the use of stemmers to strip suffixes has increased. Stemmers have been used to more accurately determine relevance between a keyword query and methods in source code for search, exploration, and bug localization. In this paper, we investigate which
images amenable to browsing and searching in digital libraries. In this paper, we propose a novel multi-pass alignment method based on Hidden Markov Models (HMM) that combines text line recognition, string alignment, and keyword spotting to cope with word substitutions, deletions, and insertions in the transcription. In a
this module and early results of CBIR enabled the combination of content-based retrieval and keyword retrieval. It made some improvements to the retrieval performance and narrowed the gap of semantics. Experimental results demonstrated that this project can to a certain extent help users more precisely retrieve to their
This paper surveys Audio Information Retrieval (AIR) using a literature review and classification of articles from 1994 to 2010 with a keyword index and article abstract in order to explore how AIR methodologies and applications have developed during this period. Based on the scope of many papers and journals of AIR
Electronic Commerce has offered a convenient way for people to go shopping on the Internet. However, it is difficult for Internet customers to select a valuable item from the great number of various products available on line. When we use a keyword and search in a EC website, the ranking algorithm of products is
some problems, they tend to retrieve the information from the Web search engines. Many business search engines are efficient at identifying the best web sites for any given keyword query. Unfortunately, the information on the web is not always correct. Moreover, different web sites often provide different information on a
detect user sentiments. The keyword-based approaches for identifying such themes fail to give satisfactory level of accuracy. Here, we address the above problems using statistical text-mining of blog entries. The crux of the analysis lies in mining quantitative information from textual entries. Once the relevant blog
This paper proposes a novel personalized news recommendation system named InfoSlim. The new system uses semantic technique to annotate news items and user preference in order to add rich metadata information into traditional keyword vector. By doing this, the similarity measure between item profile and user profile
Spatial information retrieval which is mainly catalog service based on metadata is the foundation of spatial information sharing. Today's information retrieval methods in catalog service are typically limited to keyword searches or matches of sub-strings, which do not meet user's retrieval demand. This paper presents
Our research addresses semantic retrieval of images from plethora of image dumps by imparting human cognition in the image retrieval process. Proposed architecture SIREA addresses the issues of keyword based image retrieval and content based image retrieval through semantics. Performance of semantic image retrieval is
special data record and new record model on fuzzy set are given. By calculating the membership of keyword, new fuzzy closeness functions are proposed to classify the information. Finally, examples prove that this algorithm can effectively and automatically classify input information of database, the accuracy and intelligence
subjectivity of deciding relevant documents empirically. Furthermore, a sentence selection strategy through extracting keywords is proposed. It calculated the word's query related feature through word co-occurrence window, and obtained the topic related feature through likelihood ratio, then combined the two features to extract
Tattoo images on human body have been routinely collected and used in law enforcement to assist in suspect and victim identification. However, the current practice of matching tattoos is based on keywords. Assigning keywords to individual tattoo images is both tedious and subjective. We have developed a content-based
using keywords extracted from the network. Experimental results with episodes that have different word accuracy and content showed that keywords obtained from competitive candidates were useful in retrieving similar episodes. To show relevant episodes, our method will be incorporated into PodCastle, a public web service
not able to use for Chinese documents. This paper presents an integrated algorithm, KMatch, for near-duplicate document detection of large scale Chinese Web pages. First of all, KMatch employs Chinese segmentation algorithm to prepare Chinese words into meaningful features to compress documents. Then keywords matching
The aim of the spoken term detection task is to find the occurrence of user-entered keywords in an archive of audio recordings. The kind of techniques that are used usually are vocabulary-independent, using only the acoustic information available. In this scenario, however, we rely exclusively on the acoustic model
when the sentence is analyzed. The goal is to put each noun and verb of the sentence on the right place on the tree. Taking this information into account, it is possible to solve the ambiguity problem for the query keywords and create the indicative summaries taking into account query words, and semantically related
In this paper, a new method for question classification is proposed, which employs ensemble learning algorithms to train multiple question classifiers. These component learners are combined to produce the final hypothesis. In detail, the feature spaces are obtained through extracting high-frequency keywords from
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