Search (2 results, page 1 of 1)
- Did you mean:
- themes%3a%22Preserved context index system %28PRECIS%29%22 2
-
Kim, H.H.; Kim, Y.H.: Video summarization using event-related potential responses to shot boundaries in real-time video watching (2019)
0.01
0.008069678 = product of: 0.040348392 = sum of: 0.040348392 = weight(_text_:context in 4685) [ClassicSimilarity], result of: 0.040348392 = score(doc=4685,freq=2.0), product of: 0.17622331 = queryWeight, product of: 4.14465 = idf(docFreq=1904, maxDocs=44218) 0.04251826 = queryNorm 0.22896172 = fieldWeight in 4685, product of: 1.4142135 = tf(freq=2.0), with freq of: 2.0 = termFreq=2.0 4.14465 = idf(docFreq=1904, maxDocs=44218) 0.0390625 = fieldNorm(doc=4685) 0.2 = coord(1/5)
- Abstract
- Our aim was to develop an event-related potential (ERP)-based method to construct a video skim consisting of key shots to bridge the semantic gap between the topic inferred from a whole video and that from its summary. Mayer's cognitive model was examined, wherein the topic integration process of a user evoked by a visual stimulus can be associated with long-latency ERP components. We determined that long-latency ERP components are suitable for measuring a user's neuronal response through a literature review. We hypothesized that N300 is specific to the categorization of all shots regardless of topic relevance, N400 is specific for the semantic mismatching process for topic-irrelevant shots, and P600 is specific for the context updating process for topic-relevant shots. In our experiment, the N400 component led to more negative ERP signals in response to topic-irrelevant shots than to topic-relevant shots and showed a fronto-central scalp pattern. P600 elicited more positive ERP signals for topic-relevant shots than for topic-irrelevant shots and showed a fronto-central scalp pattern. We used discriminant and artificial neural network (ANN) analyses to decode video shot relevance and observed that the ANN produced particularly high success rates: 91.3% from the training set and 100% from the test set.
-
Kim, H.H.; Kim, Y.H.: Generic speech summarization of transcribed lecture videos : using tags and their semantic relations (2016)
0.01
0.007715711 = product of: 0.038578555 = sum of: 0.038578555 = product of: 0.057867832 = sum of: 0.029064644 = weight(_text_:29 in 2640) [ClassicSimilarity], result of: 0.029064644 = score(doc=2640,freq=2.0), product of: 0.14956595 = queryWeight, product of: 3.5176873 = idf(docFreq=3565, maxDocs=44218) 0.04251826 = queryNorm 0.19432661 = fieldWeight in 2640, product of: 1.4142135 = tf(freq=2.0), with freq of: 2.0 = termFreq=2.0 3.5176873 = idf(docFreq=3565, maxDocs=44218) 0.0390625 = fieldNorm(doc=2640) 0.028803186 = weight(_text_:22 in 2640) [ClassicSimilarity], result of: 0.028803186 = score(doc=2640,freq=2.0), product of: 0.1488917 = queryWeight, product of: 3.5018296 = idf(docFreq=3622, maxDocs=44218) 0.04251826 = queryNorm 0.19345059 = fieldWeight in 2640, 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=2640) 0.6666667 = coord(2/3) 0.2 = coord(1/5)
- Date
- 22. 1.2016 12:29:41