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.

cvpr12_multipleclasslearning.pdf
PDF file

In  Computer Vision and Pattern Recognition (CVPR)

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