Date of Original Version

9-2010

Type

Article

Rights Management

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-15555-0_57

Abstract or Description

Recently, there has been increasing interests in applying aspect models (e.g., PLSA and LDA) in image segmentation. However, these models ignore spatial relationships among local topic labels in an image and suffers from information loss by representing image feature using the index of its closest match in the codebook. In this paper, we propose Topic Random Field(TRF) to tackle these two problems. Specifically, TRF defines a Markov Random Field over hidden labels of an image, to enforce the spatial coherence between topic labels for neighboring regions. Moreover, TRF utilizes a noise channel to model the generation of local image features, and avoids the off-line process of building visual codebook. We provide details of variational inference and parameter learning for TRF. Experimental evaluations on three image data sets show that TRF achieves better segmentation performance.

DOI

10.1007/978-3-642-15555-0_57

Share

COinS
 

Published In

Lecture Notes in Computer Science, 6315, 785-798.