Name
Abstract | ||
|---|---|---|
Traditional Exemplar-SVMs (ESVM) require millions of negative samples to establish a linear exemplar detector. However, Exemplar Linear Discriminant Analysis (ELDA) can achieve similar performance while avoid negative samples mining. To construct a strong object classifier, Multiple Instance Learning (MIL) is used to combine exemplar detectors and reduce annotation ambiguity. By applying MIL to Exemplar-LDA (ELDA), we simplify the training process and achieve better performance than ESVM on object detection. Moreover, exemplar models can transfer the available meta-data (segmentation, geometric structure, etc.) of training samples directly onto the detected objects, which provide more accurate and richer attributions than the detection results of a bounding box. |
Year | Venue | Field |
|---|---|---|
2016 | ICASSP | Metadata,Object detection,Annotation,Pattern recognition,Computer science,Segmentation,Artificial intelligence,Linear discriminant analysis,Classifier (linguistics),Ambiguity,Machine learning,Minimum bounding box |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
10 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Chen | 1 | 0 | 6.76 |
| Ling Cai | 2 | 0 | 1.01 |
| Yuming Zhao | 3 | 0 | 0.34 |
| Fuqiao Hu | 4 | 0 | 0.34 |