Name
Abstract | ||
|---|---|---|
Large scale, class imbalanced data classification is a challenging task that occurs frequently in several computer vision tasks such as web video retrieval. A number of algorithms have been proposed in literature that approach this problem from different perspectives (e.g. Sampling, Cost-sensitive learning, Active learning). The challenge is two fold in this task — first the data imbalance causes many classification algorithms to learn trivial classifiers that declare all test examples to be from the majority class. Second, many algorithms do not scale to large dataset sizes. We address these two issues by using two different cost-sensitive versions of Ridge Regression as our binary classifiers. We demonstrate our approach for retrieving unstructured web videos from 10 events on the benchmark TRECVID MED 12 dataset containing ≈47000 videos. We empirically show that they perform at par with state-of-the-art support vector machine based classifiers using χ2 kernels while being 30 to 60 times faster. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/WACV.2013.6475028 | WACV |
Keywords | DocType | Citations |
challenging task,different cost-sensitive version,active learning,classification algorithm,Ridge Regression,large dataset size,different perspective,Cost-sensitive learning,data imbalance,computer vision task,imbalanced datasets,class imbalanced data classification,large scale class | Conference | 3 |
PageRank | References | Authors |
0.50 | 13 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rohit Prasad | 1 | 465 | 39.06 |
| Devansh Arpit | 2 | 146 | 14.24 |
| Shuang Wu | 3 | 171 | 7.23 |
| Premkumar Natarajan | 4 | 874 | 79.46 |
| Pradeep Natarajan | 5 | 14 | 2.14 |