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2008

Unsupervised texture classification: Automatically discover and classify texture patterns

9 years 2 months ago
Unsupervised texture classification: Automatically discover and classify texture patterns
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 vector-quantized to form 'keypoints'. Then we represent the texture images by 'bagof-keypoints' vectors. By analogy text classification, we use Probabilistic Latent Semantic Indexing(PLSI) to perform unsupervised classification. The proposed approach is evaluated using the UIUC database which contains significant viewpoint and scale changes. 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.
Lei Qin, Qingfang Zheng, Shuqiang Jiang, Qingming
Added 27 Dec 2010
Updated 27 Dec 2010
Type Journal
Year 2008
Where IVC
Authors Lei Qin, Qingfang Zheng, Shuqiang Jiang, Qingming Huang, Wen Gao
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