Learning beautiful and ugly visual attributes

摘要

A method for learning visual attribute labels for images includes, from textual comments associated with a corpus of images, identifying a set of candidate textual labels that are predictive of aesthetic scores associated with images in the corpus. The candidate labels in the set are clustered into a plurality of visual attribute clusters based on similarity and each of the clusters assigned a visual attribute label. For each of the visual attribute labels, a classifier is trained using visual representations of images in the corpus and respective visual attribute labels. The visual attribute labels are evaluated, based on performance of the trained classifier. A subset of the visual attribute labels is retained, based on the evaluation. The visual attribute labels can be used in processes such as image retrieval, image labeling, and the like.

主权项

1. A method for learning visual attribute labels for images comprising:
from textual comments associated with a corpus of images, identifying a set of candidate textual labels that are predictive of aesthetic scores associated with images in the corpus; clustering the candidate labels in the set into a plurality of visual attribute clusters based on similarity and assigning each of the clusters a visual attribute label; for each of the visual attribute labels, training a visual attribute classifier using visual representations of images in the corpus and respective visual attribute labels; evaluating the visual attribute labels based on performance of the trained visual attribute classifiers, the evaluating comprising comparing performance of each of the visual attribute classifiers with a predefined threshold; and retaining a subset of the visual attribute labels based on the evaluation, including retaining the visual attribute labels for the visual attribute classifiers that meet the performance threshold; wherein at least one of the identifying a set of candidate textual labels, clustering the candidate labels, training the classifier, and evaluating the classifier performance is performed with a processor.