Search (3 results, page 1 of 1)

0.01938208 = product of:
  0.03876416 = sum of:
    0.03876416 = sum of:
      0.0075639198 = weight(_text_:a in 2640) [ClassicSimilarity], result of:
        0.0075639198 = score(doc=2640,freq=10.0), product of:
          0.053105544 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.046056706 = queryNorm
          0.14243183 = fieldWeight in 2640, product of:
            3.1622777 = tf(freq=10.0), with freq of:
              10.0 = termFreq=10.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.0390625 = fieldNorm(doc=2640)
      0.03120024 = weight(_text_:22 in 2640) [ClassicSimilarity], result of:
        0.03120024 = score(doc=2640,freq=2.0), product of:
          0.16128273 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046056706 = 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)
  0.5 = coord(1/2)

Abstract

We propose a tag-based framework that simulates human abstractors' ability to select significant sentences based on key concepts in a sentence as well as the semantic relations between key concepts to create generic summaries of transcribed lecture videos. The proposed extractive summarization method uses tags (viewer- and author-assigned terms) as key concepts. Our method employs Flickr tag clusters and WordNet synonyms to expand tags and detect the semantic relations between tags. This method helps select sentences that have a greater number of semantically related key concepts. To investigate the effectiveness and uniqueness of the proposed method, we compare it with an existing technique, latent semantic analysis (LSA), using intrinsic and extrinsic evaluations. The results of intrinsic evaluation show that the tag-based method is as or more effective than the LSA method. We also observe that in the extrinsic evaluation, the grand mean accuracy score of the tag-based method is higher than that of the LSA method, with a statistically significant difference. Elaborating on our results, we discuss the theoretical and practical implications of our findings for speech video summarization and retrieval.

Date

22. 1.2016 12:29:41

Type

a

0.0029294936 = product of:
  0.005858987 = sum of:
    0.005858987 = product of:
      0.011717974 = sum of:
        0.011717974 = weight(_text_:a in 5358) [ClassicSimilarity], result of:
          0.011717974 = score(doc=5358,freq=24.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.22065444 = fieldWeight in 5358, product of:
              4.8989797 = tf(freq=24.0), with freq of:
                24.0 = termFreq=24.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5358)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

We propose and evaluate a video summarization method based on a topic relevance model, a maximal marginal relevance (MMR), and discriminant analysis to generate a semantically meaningful video skim. The topic relevance model uses event-related potential (ERP) components to describe the process of topic relevance judgment. More specifically, the topic relevance model indicates that N400 and P600, which have been successfully applied to the mismatch process of a stimulus and the discourse-internal reorganization and integration process of a stimulus, respectively, are used for the topic mismatch process of a topic-irrelevant video shot and the topic formation process of a topic-relevant video shot. To evaluate our proposed ERP/MMR-based method, we compared the video skims generated by the ERP/MMR-based, ERP-based, and shot boundary detection (SBD) methods with ground truth skims. The results showed that at a significance level of 0.05, the ROUGE-1 scores of the ERP/MMR method are statistically higher than those of the SBD method, and the diversity scores of the ERP/MMR method are statistically higher than those of the ERP method. This study suggested that the proposed method may be applied to the construction of a video skim without operational intervention, such as the insertion of a black screen between video shots.

Type

a

0.0025370158 = product of:
  0.0050740317 = sum of:
    0.0050740317 = product of:
      0.010148063 = sum of:
        0.010148063 = weight(_text_:a in 4685) [ClassicSimilarity], result of:
          0.010148063 = score(doc=4685,freq=18.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.19109234 = fieldWeight in 4685, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4685)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.

Type

a