Name
Abstract | ||
|---|---|---|
Many multimedia applications can benefit from techniques for adapting existing classifiers to data with different distributions. One example is cross-domain video concept detection which aims to adapt concept classifiers across various video domains. In this paper, we explore two key problems for classifier adaptation: (1) how to transform existing classifier(s) into an effective classifier for a new dataset that only has a limited number of labeled examples, and (2) how to select the best existing classifier(s) for adaptation. For the first problem, we propose Adaptive Support Vector Machines (A-SVMs) as a general method to adapt one or more existing classifiers of any type to the new dataset. It aims to learn the "delta function" between the original and adapted classifier using an objective function similar to SVMs. For the second problem, we estimate the performance of each existing classifier on the sparsely-labeled new dataset by analyzing its score distribution and other meta features, and select the classifiers with the best estimated performance. The proposed method outperforms several baseline and competing methods in terms of classification accuracy and efficiency in cross-domain concept detection in the TRECVID corpus. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1291233.1291276 | ACM Multimedia 2001 |
Keywords | Field | DocType |
existing classifier,cross-domain concept detection,new dataset,delta function,estimated performance,sparsely-labeled new dataset,cross-domain video concept detection,effective classifier,classifier adaptation,adaptive svms,concept classifier,support vector machine,objective function | Pattern recognition,Random subspace method,Computer science,TRECVID,Support vector machine,Dirac delta function,Artificial intelligence,Margin classifier,Classifier (linguistics),Machine learning,Quadratic classifier | Conference |
Citations | PageRank | References |
320 | 12.66 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Yang | 1 | 937 | 37.42 |
| Rong Yan | 2 | 2019 | 104.99 |
| Alexander G. Hauptmann | 3 | 7472 | 558.23 |