Unsupervised Object Class Discovery via Saliency-Guided Multiple Class Learning

Discovering object classes from images in a fully unsupervised
way is an intrinsically ambiguous task; saliency
detection approaches however ease the burden on unsupervised
learning. We develop an algorithm for simultaneously
localizing objects and discovering object classes
via bottom-up (saliency-guided) multiple class learning
(bMCL), and make the following contributions: (1) saliency
detection is adopted to convert unsupervised learning into
multiple instance learning, formulated as bottom-up multiple
class learning (bMCL); (2) we utilize the Discriminative
EM (DiscEM) to solve our bMCL problem and show
DiscEM’s connection to the MIL-Boost method[34]; (3) localizing
objects, discovering object classes, and training
object detectors are performed simultaneously in an integrated
framework; (4) significant improvements over the
existing methods for multi-class object discovery are observed.
In addition, we show single class localization as
a special case in our bMCL framework and we also demonstrate
the advantage of bMCL over purely data-driven
saliency methods.