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Unsupervised texture classification: Automatically discover and classify texture patterns
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DOI：10.1016/j.imavis.2007.08.003.png)
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In this paper, we present a novel approach to classify texture collections. This approach does not require experts to provide annotated training set. Given the image collection, we extract a set of invariant descriptors from each image. The descriptors of all images are veetor-quantized to form 'keypoints'. Then we represent the texture images by 'bag-of-keypoints' vectors. By analogy text classification, we use Probabilistic Latent Semantic Indexing (PLSI) and Non-negative Matrix Factorization (NMF) to perform unsupervised classification. The proposed approach is evaluated using the UIUC database which contains significant viewpoint and scale changes. We also report the results for simultaneously classifying I I I texture categories using the Brodatz database. The performances of classifying new images using the parameters learnt from the unannotated image collection are also presented. The experiment results clearly demonstrate that the approach is robust to scale and viewpoint changes, and achieves good classification accuracy even without annotated training set. (C) 2007 Elsevier B.V. All rights reserved.
Keyword:
unsupervised texture classification
NMF
PLSI
invariant descriptor
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分享收藏Representing and recognizing the visual appearance of materials using three-dimensional textons
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分享收藏Unsupervised learning by probabilistic latent semantic analysis基于概率潜在语义分析的无监督学习.png)
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