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Abstract
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Article Information:
Modeling the Relationship between Texture Semantics and Textile Images
Xiaohui Wang, Jia Jia, Yongxin Wang and Lianhong Cai
Corresponding Author: Xiaohui Wang
Submitted: 2011 July, 20
Accepted: 2011 September, 07
Published: 2011 September, 20 |
Abstract:
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Texture semantics, which is the kind of feelings that the texture feature of an image would arouse
in people, is important in texture analysis. In this paper, we study the relationship between texture semantics
and textile images, and propose a novel parametric mapping model to predict texture semantics from textile
images. To represent rich texture semantics and enable it to participate in computation, 2D continuous semantic
space, where the axes correspond to hard-soft and warm-cool, is first adopted to quantitatively describe texture
semantics. Then texture features of textile images are extracted using Gabor decomposition. Finally, the
mapping model between texture features and texture semantics in the semantic space is built using three
different methods: linear regression, k-nearest neighbor (KNN) and multi-layered perceptron (MLP). The
performance of the proposed mapping model is evaluated with a dataset of 1352 textile images. The results
confirm that the mapping model is effective and especially KNN and MLP reach the good performance. We
further apply the mapping model to two applications: automatic textile image annotation with texture semantics
and textile image search based on texture semantics. The subjective experimental results are consistent with
human perception, which verifies the effectiveness of the proposed mapping model. The proposed model and
its applications can be applied to various automation systems in commercial textile industry.
Key words: Image search, mapping model, textile images, texture semantics, semantic space, ,
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Cite this Reference:
Xiaohui Wang, Jia Jia, Yongxin Wang and Lianhong Cai, . Modeling the Relationship between Texture Semantics and Textile Images. Research Journal of Applied Sciences, Engineering and Technology, (09): 977-985.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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