Semi-supervised price tag detection

A computer program product comprising a non-transitory computer-readable storage medium having program code embodied thereon, the program code executable by at least one hardware processor to:

• train a price tag detector that comprises a gross feature detector and a classifier, to automatically detect a price tag in an image, by:
• a) training the gross feature detector using a supervised learning process with a set of labeled images, and
• b) training the classifier using a two-phase hybrid learning process comprising:
• applying an initial supervised learning phase using the set of labeled images, and yielding a semi-trained version of the classifier, and
• applying a subsequent unsupervised learning phase using a set of unlabeled images, yielding a fully trained version of the classifier,
• wherein applying the unsupervised learning phase comprises:
• for each unlabeled image:
• i) detecting multiple price tag hypotheses using the gross feature detector,
• ii) classifying each price tag hypothesis using the semi-trained classifier,
• iii) rating each classification based contextual data extracted from the unlabeled image,
• iv) retraining the semi-trained classifier with the rated classifications, and
• repeating steps ii) through iv) until the reclassification converges thereby yielding the trained classifier.

A system, comprising:

• at least one hardware processor;
• and
• a non-transitory memory device having embodied thereon program code executable by said at least one hardware processor to:
• train a price tag detector that comprises a gross feature detector and a classifier, to automatically detect a price tag in an image, by:
• a) training the gross feature detector using a supervised learning process with a set of labeled images, and
• b) training the classifier using a two-phase hybrid learning process comprising:
• applying an initial supervised learning phase using the set of labeled images, and yielding a semi-trained version of the classifier, and
• applying a subsequent unsupervised learning phase using a set of unlabeled images, yielding a fully trained version of the classifier,
• wherein applying the unsupervised learning phase comprises:
• for each unlabeled image:
• i) detecting multiple price tag hypotheses using the gross feature detector,
• ii) classifying each price tag hypothesis using the semi-trained classifier,
• iii) rating each classification based contextual data extracted from the unlabeled image,
• iv) retraining the semi-trained classifier with the rated classifications, and
• repeating steps ii) through iv) until the reclassification converges thereby yielding the trained classifier.