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Combined morphological-spectral unsupervised image segmentation

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2 Author(s)
O'Callaghan, R.J. ; Visual Inf. Lab., Guildford, UK ; Bull, D.R.

The goal of segmentation is to partition an image into disjoint regions, in a manner consistent with human perception of the content. For unsupervised segmentation of general images, however, there is the competing requirement not to make prior assumptions about the scene. Here, a two-stage method for general image segmentation is proposed, which is capable of processing both textured and nontextured objects in a meaningful fashion. The first stage extracts texture features from the subbands of the dual-tree complex wavelet transform. Oriented median filtering is employed, to circumvent the problem of texture feature response at step edges in the image. From the processed feature images, a perceptual gradient function is synthesised, whose watershed transform provides an initial segmentation. The second stage of the algorithm groups together these primitive regions into meaningful objects. To achieve this, a novel spectral clustering technique is proposed, which introduces the weighted mean cut cost function for graph partitioning. The ability of the proposed algorithm to generalize across a variety of image types is demonstrated.

Published in:

Image Processing, IEEE Transactions on  (Volume:14 ,  Issue: 1 )

Date of Publication:

Jan. 2005

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