Search (8 results, page 1 of 1)

0.023835853 = product of:
  0.047671705 = sum of:
    0.047671705 = product of:
      0.09534341 = sum of:
        0.09534341 = weight(_text_:e.g in 3452) [ClassicSimilarity], result of:
          0.09534341 = score(doc=3452,freq=4.0), product of:
            0.23393378 = queryWeight, product of:
              5.2168427 = idf(docFreq=651, maxDocs=44218)
              0.044842023 = queryNorm
            0.40756583 = fieldWeight in 3452, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.2168427 = idf(docFreq=651, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3452)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

With the emergence of Web 2.0, sharing personal content, communicating ideas, and interacting with other online users in Web 2.0 communities have become daily routines for online users. User-generated data from Web 2.0 sites provide rich personal information (e.g., personal preferences and interests) and can be utilized to obtain insight about cyber communities and their social networks. Many studies have focused on leveraging user-generated information to analyze blogs and forums, but few studies have applied this approach to video-sharing Web sites. In this study, we propose a text-based framework for video content classification of online-video sharing Web sites. Different types of user-generated data (e.g., titles, descriptions, and comments) were used as proxies for online videos, and three types of text features (lexical, syntactic, and content-specific features) were extracted. Three feature-based classification techniques (C4.5, Naïve Bayes, and Support Vector Machine) were used to classify videos. To evaluate the proposed framework, user-generated data from candidate videos, which were identified by searching user-given keywords on YouTube, were first collected. Then, a subset of the collected data was randomly selected and manually tagged by users as our experiment data. The experimental results showed that the proposed approach was able to classify online videos based on users' interests with accuracy rates up to 87.2%, and all three types of text features contributed to discriminating videos. Support Vector Machine outperformed C4.5 and Naïve Bayes techniques in our experiments. In addition, our case study further demonstrated that accurate video-classification results are very useful for identifying implicit cyber communities on video-sharing Web sites.

0.020225393 = product of:
  0.040450785 = sum of:
    0.040450785 = product of:
      0.08090157 = sum of:
        0.08090157 = weight(_text_:e.g in 1247) [ClassicSimilarity], result of:
          0.08090157 = score(doc=1247,freq=2.0), product of:
            0.23393378 = queryWeight, product of:
              5.2168427 = idf(docFreq=651, maxDocs=44218)
              0.044842023 = queryNorm
            0.34583107 = fieldWeight in 1247, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.2168427 = idf(docFreq=651, maxDocs=44218)
              0.046875 = fieldNorm(doc=1247)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In this era of the Internet and distributed, multimedia computing, new and emerging classes of information systems applications have swept into the lives of office workers and people in general. From digital libraries, multimedia systems, geographic information systems, and collaborative computing to electronic commerce, virtual reality, and electronic video arts and games, these applications have created tremendous opportunities for information and computer science researchers and practitioners. As applications become more pervasive, pressing, and diverse, several well-known information retrieval (IR) problems have become even more urgent. Information overload, a result of the ease of information creation and transmission via the Internet and WWW, has become more troublesome (e.g., even stockbrokers and elementary school students, heavily exposed to various WWW search engines, are versed in such IR terminology as recall and precision). Significant variations in database formats and structures, the richness of information media (text, audio, and video), and an abundance of multilingual information content also have created severe information interoperability problems -- structural interoperability, media interoperability, and multilingual interoperability.

0.016685098 = product of:
  0.033370197 = sum of:
    0.033370197 = product of:
      0.06674039 = sum of:
        0.06674039 = weight(_text_:e.g in 4276) [ClassicSimilarity], result of:
          0.06674039 = score(doc=4276,freq=4.0), product of:
            0.23393378 = queryWeight, product of:
              5.2168427 = idf(docFreq=651, maxDocs=44218)
              0.044842023 = queryNorm
            0.28529608 = fieldWeight in 4276, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.2168427 = idf(docFreq=651, maxDocs=44218)
              0.02734375 = fieldNorm(doc=4276)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Advanced technology has resulted in the generation of about one million terabytes of information every year. Ninety-reine percent of this is available in digital format (Keim, 2001). More information will be generated in the next three years than was created during all of previous human history (Keim, 2001). Collecting information is no longer a problem, but extracting value from information collections has become progressively more difficult. Various search engines have been developed to make it easier to locate information of interest, but these work well only for a person who has a specific goal and who understands what and how information is stored. This usually is not the Gase. Visualization was commonly thought of in terms of representing human mental processes (MacEachren, 1991; Miller, 1984). The concept is now associated with the amplification of these mental processes (Card, Mackinlay, & Shneiderman, 1999). Human eyes can process visual cues rapidly, whereas advanced information analysis techniques transform the computer into a powerful means of managing digitized information. Visualization offers a link between these two potent systems, the human eye and the computer (Gershon, Eick, & Card, 1998), helping to identify patterns and to extract insights from large amounts of information. The identification of patterns is important because it may lead to a scientific discovery, an interpretation of clues to solve a crime, the prediction of catastrophic weather, a successful financial investment, or a better understanding of human behavior in a computermediated environment. Visualization technology shows considerable promise for increasing the value of large-scale collections of information, as evidenced by several commercial applications of TreeMap (e.g., http://www.smartmoney.com) and Hyperbolic tree (e.g., http://www.inxight.com) to visualize large-scale hierarchical structures. Although the proliferation of visualization technologies dates from the 1990s where sophisticated hardware and software made increasingly faster generation of graphical objects possible, the role of visual aids in facilitating the construction of mental images has a long history. Visualization has been used to communicate ideas, to monitor trends implicit in data, and to explore large volumes of data for hypothesis generation. Imagine traveling to a strange place without a map, having to memorize physical and chemical properties of an element without Mendeleyev's periodic table, trying to understand the stock market without statistical diagrams, or browsing a collection of documents without interactive visual aids. A collection of information can lose its value simply because of the effort required for exhaustive exploration. Such frustrations can be overcome by visualization.

0.009113212 = product of:
  0.018226424 = sum of:
    0.018226424 = product of:
      0.03645285 = sum of:
        0.03645285 = weight(_text_:22 in 2733) [ClassicSimilarity], result of:
          0.03645285 = score(doc=2733,freq=2.0), product of:
            0.15702912 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.044842023 = queryNorm
            0.23214069 = fieldWeight in 2733, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=2733)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 3.2009 17:57:50

0.0075943437 = product of:
  0.0151886875 = sum of:
    0.0151886875 = product of:
      0.030377375 = sum of:
        0.030377375 = weight(_text_:22 in 7469) [ClassicSimilarity], result of:
          0.030377375 = score(doc=7469,freq=2.0), product of:
            0.15702912 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.044842023 = queryNorm
            0.19345059 = fieldWeight in 7469, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=7469)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Source

IEEE transactions on systems, man and cybernetics. 22(1992) no.5, S.865-884

0.0075943437 = product of:
  0.0151886875 = sum of:
    0.0151886875 = product of:
      0.030377375 = sum of:
        0.030377375 = weight(_text_:22 in 5259) [ClassicSimilarity], result of:
          0.030377375 = score(doc=5259,freq=2.0), product of:
            0.15702912 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.044842023 = queryNorm
            0.19345059 = fieldWeight in 5259, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5259)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 7.2006 14:26:01

0.0075943437 = product of:
  0.0151886875 = sum of:
    0.0151886875 = product of:
      0.030377375 = sum of:
        0.030377375 = weight(_text_:22 in 5276) [ClassicSimilarity], result of:
          0.030377375 = score(doc=5276,freq=2.0), product of:
            0.15702912 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.044842023 = queryNorm
            0.19345059 = fieldWeight in 5276, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5276)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 7.2006 16:14:37

0.0075943437 = product of:
  0.0151886875 = sum of:
    0.0151886875 = product of:
      0.030377375 = sum of:
        0.030377375 = weight(_text_:22 in 2753) [ClassicSimilarity], result of:
          0.030377375 = score(doc=2753,freq=2.0), product of:
            0.15702912 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.044842023 = queryNorm
            0.19345059 = fieldWeight in 2753, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2753)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 3.2009 18:50:30