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A Generative Model for Parts-based Object Segmentation

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http://books.nips.cc/papers/files/nips25/NIPS2012_0057.pdf
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 25
EditorsP. Bartlett, F.C.N. Pereira, C.J.C. Burges, L. Bottou, K.Q. Weinberger
Pages100-107
Number of pages8
Publication statusPublished - 2012

Abstract

The Shape Boltzmann Machine (SBM) [1] has recently been introduced as a state-of-the-art model of foreground/background object shape. We extend the SBM to account for the foreground object’s parts. Our new model, the Multinomial SBM (MSBM), can capture both local and global statistics of part shapes accurately. We combine the MSBM with an appearance model to form a fully generative model of images of objects. Parts-based object segmentations are obtained simply by performing probabilistic inference in the model. We apply the model to two challenging datasets which exhibit significant shape and appearance variability, and find that it obtains results that are comparable to the state-of-the-art.

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