Name
Abstract | ||
|---|---|---|
In this paper, we propose a visual saliency algorithm for automatically finding the topic transition points in an educational video. First, we propose a method for assigning a saliency score to each word extracted from an educational video. We design several mid-level features that are indicative of visual saliency. The optimal feature combination strategy is learnt from a Rank-SVM to obtain an overall visual saliency score for all the words. Second, we use these words and their saliency scores to find the probability of a slide being a topic transition slide. On a test set of 10 instructional videos (12 hours), the F-score of the proposed algorithm in retrieving topic-transition slides is 0.17 higher than that of Latent Dirichlet Allocation (LDA)based methods. The proposed algorithm enables demarcation of an instructional video along the lines of ‘table of content’/‘sections’ for a written document and has applications in efficient video navigation, indexing, search and summarization. User studies also demonstrate statistically significant improvement in across-topic navigation using the proposed algorithm. |
Year | Venue | Field |
|---|---|---|
2015 | EDM | Automatic summarization,Latent Dirichlet allocation,Salience (neuroscience),Computer science,Search engine indexing,Artificial intelligence,Natural language processing,Vocabulary,Visual perception,Salient,Test set |
DocType | Citations | PageRank |
Conference | 2 | 0.46 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ankit Gandhi | 1 | 16 | 2.92 |
| Arijit Biswas | 2 | 747 | 38.43 |
| Om Deshmukh | 3 | 56 | 10.55 |