Name
Abstract | ||
|---|---|---|
In this paper we propose an in-depth evaluation of the performance of video descriptors to multimodal video genre categorization. We discuss the perspective of designing appropriate late fusion techniques that would enable to attain very high categorization accuracy, close to the one achieved with user-based text information. Evaluation is carried out in the context of the 2012 Video Genre Tagging Task of the MediaEval Benchmarking Initiative for Multimedia Evaluation, using a data set of up to 15.000 videos (3,200 hours of footage) and 26 video genre categories specific to web media. Results show that the proposed approach significantly improves genre categorization performance, outperforming other existing approaches. The main contribution of this paper is in the experimental part, several valuable interesting findings are reported that motivate further research on video genre classification. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CBMI.2013.6576545 | 2013 11TH INTERNATIONAL WORKSHOP ON CONTENT-BASED MULTIMEDIA INDEXING (CBMI 2013) |
Keywords | Field | DocType |
image classification,support vector machines,visualization,histograms,feature extraction,internet | Computer vision,Categorization,Video retrieval,Information retrieval,Computer science,Artificial intelligence,Contextual image classification,Benchmarking,The Internet | Conference |
ISSN | Citations | PageRank |
1949-3983 | 5 | 0.42 |
References | Authors | |
20 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ionut Mironica | 1 | 90 | 8.63 |
| Bogdan Ionescu | 2 | 458 | 56.67 |
| Peter Knees | 3 | 594 | 51.71 |
| Patrick Lambert | 4 | 31 | 3.69 |