Academic Journal
In defence of negative mining for annotating weakly labelled data
| العنوان: | In defence of negative mining for annotating weakly labelled data |
|---|---|
| المؤلفون: | Parthipan Siva, Chris Russell, Tao Xiang |
| المساهمون: | The Pennsylvania State University CiteSeerX Archives |
| المصدر: | http://www.psiva.ca/Publications/ECCV2012.pdf. |
| سنة النشر: | 2012 |
| المجموعة: | CiteSeerX |
| مصطلحات موضوعية: | Weakly Supervised Learning, Multiple-Instance Learning, Negative Mining, Automatic Annotation |
| الوصف: | We propose a novel approach to annotating weakly labelled data. In contrast to many existing approaches that perform annotation by seeking clusters of self-similar exemplars (minimising intra-class vari-ance), we perform image annotation by selecting exemplars that have never occurred before in the much larger, and strongly annotated, nega-tive training set (maximising inter-class variance). Compared to existing methods, our approach is fast, robust, and obtains state of the art results on two challenging data-sets – voc2007 (all poses), and the msr2 action data-set, where we obtain a 10 % increase. Moreover, this use of nega-tive mining complements existing methods, that seek to minimize the intra-class variance, and can be readily integrated with many of them. |
| نوع الوثيقة: | text |
| وصف الملف: | application/pdf |
| اللغة: | English |
| Relation: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.674.9413; http://www.psiva.ca/Publications/ECCV2012.pdf |
| الاتاحة: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.674.9413 http://www.psiva.ca/Publications/ECCV2012.pdf |
| Rights: | Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
| رقم الانضمام: | edsbas.CD0E1A54 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |