Visualize two-class localization (such as Graz-02) and find the precision=recall point.
BK = BLOCK_VISLOC() Initializes the block with the default options.
BK = BLOCK_VISLOC(BK) Executes the block with options and inputs BK.
Required input:
- db
The database.
- prediction
The output of the localization classifier.
Options:
- bk.seg_prefix
The prefix path to the ground truth segmentations (Required).
- bk.seg_ext
The extension of each ground truth segmentation (Required).
- bk.cat_ids
A mapping from category names in the database to values in the ground truth segmentation images (Required).
- bk.num_levels
The number of thresholds at which to evaluate performance. Default 40.
- bk.levels
The actual thresholds to evalutate performance. Default [] uses num_levels to determine the thresholds automatically.
Fetchable attributes:
- report
A structure containing a report on the results which has fields: seg_ids: The training segment ids. tp: The raw true positives. fp: The raw false positives. tn: The raw true negatives. fn: The raw false negatives. levels: The levels chosen. eer: The precision = recall point. precision: Precision for each level. recall: Recall for each leve.
- eer_level
The precision = recall point.