Multiresolution Histograms and their Use for Texture Classification

The histogram of image intensities is used extensively for the retrieval of images from visual databases. An obvious way to extend this feature is to compute the histograms of multiple resolutions of an image. Both this extension and the plain histogram are fast to compute, space efficient, invariant to rigid motions, and robust to noise. In addition, the histograms over multiple image resolutions directly encode texture information. We describe a simple yet novel matching algorithm based on this extension. We evaluate it on two texture databases against algorithms based on five widely used texture features. We show that with our simple algorithm, we achieve or exceed the performance, robustness, and efficiency of more complicated features. 1 The Multiresolution Histogram The histogram of image intensities has proven to be a robust and efficient representation for indexing visual databases [15]. Histograms, however, don’t encode texture information. The multiresolution decomposition of an image does encode texture information. The histograms of Gaussian blurred versions of an image, as shown in Fig. 1, encode the interactions between intensities of neighboring parts of the image. We call this sequence of histograms the multiresolution histogram. This representation retains many important properties of the histogram. It is fast to compute, space efficient, and invariant to rigid motions. The inherent blurring also makes it robust to noise. All these properties make this an effective texture feature. In this work we make the following contributions: (1) We present a simple yet novel texture feature based on the multiresolution histogram. (2) We evaluate the performance of our feature. It gives excellent results on two texture databases while maintaining considerable robustness to illumination. (3) We compare our feature with five of the most commonly used texture features. We show that our simple feature is comparable or better in terms of discriminability, robustness, and efficiency.

[1]  A. J. Stam.  Some Inequalities Satisfied by the Quantities of Information of Fisher and Shannon , 1959, Inf. Control..

[2]  Azriel Rosenfeld,et al.  A Comparative Study of Texture Measures for Terrain Classification , 1975, IEEE Transactions on Systems, Man, and Cybernetics.

[3]  Edward H. Adelson,et al.  The Laplacian Pyramid as a Compact Image Code , 1983, IEEE Trans. Commun..

[4]  J. van Wijk,et al.  Spot noise texture synthesis for data visualization , 1991, SIGGRAPH.

[5]  Anil K. Jain,et al.  A multi-channel filtering approach to texture segmentation , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Anil K. Jain,et al.  Texture classification and segmentation using multiresolution simultaneous autoregressive models , 1992, Pattern Recognit..

[7]  Jian Fan,et al.  Texture Classification by Wavelet Packet Signatures , 1993, MVA.

[8]  Christos Faloutsos,et al.  QBIC project: querying images by content, using color, texture, and shape , 1993, Electronic Imaging.

[9]  Jungwoo Lee,et al.  Multiresolution video indexing for subband coded video databases , 1994, Electronic Imaging.

[10]  James R. Bergen,et al.  Pyramid-based texture analysis/synthesis , 1995, Proceedings., International Conference on Image Processing.

[11]  Lewis D. Griffin.  Scale-imprecision space , 1997, Image and Vision Computing.

[12]  Jing Huang,et al.  Image indexing using color correlograms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  Anil K. Jain,et al.  Object detection using gabor filters , 1997, Pattern Recognit..

[14]  R. Manmatha,et al.  Retrieving images by appearance , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[15]  Shree K. Nayar,et al.  Reflectance and texture of real-world surfaces , 1999, TOGS.

[16]  Shree K. Nayar,et al.  Spatial information in multiresolution histograms , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.