Refereed full papers (journals, book chapters, international conferences)

1994

@inproceedings{SmC1994,
	vgclass =	{refpap},
	vgproject =	{cbir},
	author =	{John R. Smith and Shih-Fu Chang},
	title =	{Transform Features for Texture Classification and
	Discrimination in Large Image Databases},
	booktitle =	{IEEE International Conference on Image Processing (ICIP'94)},
	address =	{Austin, TX, USA},
	pages =	{407--411},
	month =	{November~13--16},
	year =	{1994},
	url =	{ftp://ftp.ee.columbia.edu/pub/CTR-Research/advent/public/papers/94/smith94b.pdf},
	url1 =	{ftp://ftp.ee.columbia.edu/pub/CTR-Research/advent/public/papers/94/smith94b.ps.gz},
	abstract =	{This paper proposes a method for classification and
	discrimination of textures based on the energies of image subbands. We show
	that even with this relatively simple feature set, effective texture
	discrimination can be achieved. In this paper, subband-energy feature sets
	extracted from the following typical image decompositions are compared:
	wavelet subband, uniform subband, discrete cosine transform (DCT), and
	spatial partitioning. We report that over 90\% correct classification was
	attained using the feature set in classifying the full Brodatz [3]
	collection of 112 textures. Furthermore, the subband energy-based feature
	set can be readily applied to a system for indexing images by texture
	content in image databases, since the features can be extracted directly
	from spatial-frequency decomposed image data.

	In this paper, we also show that to construct a suitable space for
	discrimination, Fisher Discrimination Analysis [5] can be used to compact
	the original features into a set of uncorrelated linear discriminant
	functions. This procedure makes it easier to perform texture-based searches
	in a database by reducing the dimensionality of the discriminant space. We
	also examine the effects of varying training class size, the number of
	training classes, the dimension of the discriminant space and number of
	energy measures used for classification. We hope that the excellent
	performance for texture discrimination of these simple energy-based
	features will allow images in a database to be efficiently and effectively
	indexed by contents of their textured regions.},
}