Name
Abstract | ||
|---|---|---|
With the explosive increase in the consumption of multimedia content in recent years, the field of media interestingness analysis has gained a lot of attention. This paper tackles the problem of image interestingness in videos and proposes a novel algorithm based on pairwise-comparisons of frames to rank all frames in a video. Experiments performed on the Predicting Media Interestingness dataset, affirm its effectiveness over existing solutions. In terms of the official metric i.e. Mean Average Precision at 10, it outperforms the previous state-of-the-art (to the best of our knowledge) on this dataset. Additional results on video interestingness substantiate the flexibility and performance reliability of our approach.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3206025.3206078 | ICMR '18: International Conference on Multimedia Retrieval
Yokohama
Japan
June, 2018 |
Keywords | Field | DocType |
Media Interestingness, Image Interestingness, Pairwise Comparisons, Artificial Neural Network | Pairwise comparison,Ranking,Computer science,Artificial intelligence,Artificial neural network,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-5046-4 | 0 | 0.34 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jayneel Parekh | 1 | 0 | 0.34 |
| Harshvardhan Tibrewal | 2 | 1 | 0.69 |
| Sanjeel Parekh | 3 | 3 | 2.48 |