Abstract
This paper introduces an efficient way of representing textures using connected regions which are formed by coherent multi-scale over-segmentations. We show that the recently introduced covariance-based similarity measure, initially applied on rectangular windows, can be used with our newly devised, irregular structure-coherent patches; increasing the discriminative power and consistency of the texture representation. Furthermore, by treating texture in multiple scales, we allow for an implicit encoding of the spatial and statistical texture properties which are persistent across scale. The meaningfulness and efficiency of the covariance based texture representation is verified utilizing a simple binary segmentation method based on min-cut. Our experiments show that the proposed method, despite the low dimensional representation in use, is able to effectively discriminate textures and that its performance compares favorably with the state of the art.
The research has been supported by the Austrian Science Foundation (FWF) under the grant S9101, and the European Union projects MOVEMENT (IST-2003-511670), Robots@home (IST-045350), and MUSCLE (FP6-507752).
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Wildenauer, H., Mičušík, B., Vincze, M. (2007). Efficient Texture Representation Using Multi-scale Regions. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76386-4_5
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DOI: https://doi.org/10.1007/978-3-540-76386-4_5
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