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Table 2. Classifler accuracies. Before: The original detection results on all the shots, after: after the removal of anchor, commercial and delimiter shots. Numbers show the number of shots detected correctly over all the detected shots. For outdoors due to the large number of images half of the data was truthed. Originally the number of detected outdoor shots was 5776 after removing anchors, delimiters and comercials.
2004
"... In PAGE 7: ... These classiflers are errorful. As shown in Table2 removing the delimiters increases the accuracy of detections, but overall accuracy is very low. Our goal is to understand how visual information even if imperfect can improve retrieval results.... ..."
Cited by 7
Table 2. Classifler accuracies. Before: The original detection results on all the shots, after: after the removal of anchor, commercial and delimiter shots. Numbers show the number of shots detected correctly over all the detected shots. For outdoors due to the large number of images half of the data was truthed. Originally the number of detected outdoor shots was 5776 after removing anchors, delimiters and comercials.
2004
"... In PAGE 7: ... These classiflers are errorful. As shown in Table2 removing the delimiters increases the accuracy of detections, but overall accuracy is very low. Our goal is to understand how visual information even if imperfect can improve retrieval results.... ..."
Cited by 7
Table 2. Classifler accuracies. Before: The original detection results on all the shots, after: after the removal of anchor, commercial and delimiter shots. Numbers show the number of shots detected correctly over all the detected shots. For outdoors due to the large number of images half of the data was truthed. Originally the number of detected outdoor shots was 5776 after removing anchors, delimiters and comercials.
2004
"... In PAGE 7: ... These classiflers are errorful. As shown in Table2 removing the delimiters increases the accuracy of detections, but overall accuracy is very low. Our goal is to understand how visual information even if imperfect can improve retrieval results.... ..."
Cited by 7
Table 1. Comparison of Visualization Tools Name Platform Data Format Domain
"... In PAGE 1: ... In this section we describe a few annotation tools commonly used in document image analysis, speech recognition, linguistics, information retrieval, video analysis, geographic system, and statistics. In Table1 , we provide a comparison of these tools. Further author information: (Send correspondence to T.... ..."
Table 1: Information filtering vs. information retrieval
"... In PAGE 5: ...Differences between IF and IR are described in Table1 . The retrieval models described above are applied to IF [7], [11].... ..."
Table 1: Differences between Web Information Retrieval and traditional Information Retrieval
Table 1: Comparison of Visualization Tools Name Platform Data Format Domain
"... In PAGE 3: ... In this section we describe a few annotation tools commonly used in document image analysis, speech recognition, linguistics, information retrieval, video analysis, geographic systems, and statistics. In Table1 we provide a comparison of these tools. 2.... ..."
Table 1: Task support by the three information visualization
1998
"... In PAGE 7: ... It is then up to the application to implement the tasks. Table1 summa- rizes how the three information visualization designs support implementing these tasks. This evaluation shows that the way a certain in- formation visualization design visualizes information may make it impossible to support a certain task.... ..."
Cited by 15
Table 1: Overview of the submissions to the photographic retrieval task. run id w/ text. inf. trained on MAP comment
"... In PAGE 2: ... These features were extracted for all images and then the feature weights were trained according to [7]. As can be seen in Table1 , textual information greatly helps to achieve a much more precise retrieval result, which was to be expected. In the visual-only runs, maximum entropy training also clearly helps to improve the precision.... ..."
Table 2. Representation of information in Visual Watermark 1
"... In PAGE 5: ... The length and binary representation of each piece of information (time, date, content, etc.) is included in Table2 . As there is no industry standard, the binary values are arbitrarily chosen to guarantee uniqueness in each field.... ..."
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