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Enriching texture analysis with semantic data

Matthews, Tim, Nixon, Mark S. and Niranjan, Mahesan (2013) Enriching texture analysis with semantic data At IEEE Conference on Computer Vision and Pattern Recognition, United States. 25 - 27 Jun 2013.

Record type: Conference or Workshop Item (Paper)


We argue for the importance of explicit semantic modelling in human-centred texture analysis tasks such as retrieval, annotation, synthesis, and zero-shot learning.

To this end, low-level attributes are selected and used to define a semantic space for texture.
319 texture classes varying in illumination and rotation are positioned within this semantic space using a pairwise relative comparison procedure. Visual features used by existing texture descriptors are then assessed in terms of their correspondence to the semantic space. Textures with strong presence of attributes connoting randomness and complexity are shown to be poorly modelled by existing descriptors.

In a retrieval experiment semantic descriptors are shown to outperform visual descriptors. Semantic modelling of texture is thus shown to provide considerable value in both feature selection and in analysis tasks.

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Accepted/In Press date: 16 April 2013
Published date: 25 June 2013
Venue - Dates: IEEE Conference on Computer Vision and Pattern Recognition, United States, 2013-06-25 - 2013-06-27
Keywords: computer vision, texture, semantics
Organisations: Vision, Learning and Control


Local EPrints ID: 352685
PURE UUID: 09163fd9-4c60-4448-bc29-3742a18ecf92

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Date deposited: 20 May 2013 14:20
Last modified: 18 Jul 2017 04:12

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Author: Tim Matthews
Author: Mark S. Nixon

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