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John R. Smith, Shih-Fu Chang. Transform Features for Texture Classification and Discrimination in Large Image Databases. In IEEE International Conference on Image Processing (ICIP), Austin, TX, November 1994.

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In this paper we propose a technique for segmenting images by texture content with application to indexing images in a large image database. Using a quad-tree decomposition, texture features are extracted from spatial blocks at a hierarchy of scales in each image. The quad-tree is grown by iteratively testing conditions for splitting parent blocks based on texture content of children blocks. While this approach does not achieve smooth identification of texture region borders, homogeneous blocks of texture are extracted which can be used in a database index. Furthermore, this technique performs the segmentation directly using image spatial-frequency data. In the segmentation reported here, texture features are extracted from the wavelet representation of the image. This method however, can use other subband decompositions including Discrete Cosine Transform (DCT), which has been adopted by the JPEG standard for image coding. This makes our segmentation method extremely applicable to databases containing compressed image data. We show application of the texture segmentation towards providing a new method for searching for images in large image databases using "Query-by-texture."


John_R. Smith

BibTex Reference

   Author = {Smith, John R. and    Chang, Shih-Fu},
   Title = {Transform Features for Texture Classification and       Discrimination in Large Image Databases},
   BookTitle = {IEEE          International Conference on Image Processing (ICIP)},
   Address = {Austin, TX},
   Month = {November},
   Year = {1994}

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