07-10-2014, 10:41 AM
Mining Weakly Labeled Web Facial Images
for Search-Based Face Annotation
Mining Weakly.pdf (Size: 1.65 MB / Downloads: 34)
Abstract—
This paper investigates a framework of search-based face annotation (SBFA) by mining weakly labeled facial images that
are freely available on the World Wide Web (WWW). One challenging problem for search-based face annotation scheme is how to
effectively perform annotation by exploiting the list of most similar facial images and their weak labels that are often noisy and
incomplete. To tackle this problem, we propose an effective unsupervised label refinement (ULR) approach for refining the labels of
web facial images using machine learning techniques. We formulate the learning problem as a convex optimization and develop
effective optimization algorithms to solve the large-scale learning task efficiently. To further speed up the proposed scheme, we also
propose a clustering-based approximation algorithm which can improve the scalability considerably. We have conducted an extensive
set of empirical studies on a large-scale web facial image testbed, in which encouraging results showed that the proposed ULR
algorithms can significantly boost the performance of the promising SBFA scheme.
INTRODUCTION
DUE to the popularity of various digital cameras and the
rapid growth of social media tools for internet-based
photo sharing [1], recent years have witnessed an explosion
of the number of digital photos captured and stored by
consumers. A large portion of photos shared by users on the
Internet are human facial images. Some of these facial
images are tagged with names, but many of them are not
tagged properly. This has motivated the study of auto face
annotation, an important technique that aims to annotate
facial images automatically
CONCLUSIONS
This paper investigated a promising search-based face
annotation framework, in which we focused on tackling
the critical problem of enhancing the label quality and