Complex Textures Classification with Edge Information

We introduce a novel texture description scheme and demonstrate it with our fast similarity search technique for content-based retrieval and navigation applications. The texture representation uses a combination of edge and region statistics. It is compared with the Multi-Resolution Simultaneous Auto-Regressive Model and Statistical Geometrical Features techniques using the entire Brodatz texture set and on a collection of more complex texture images obtained from a product catalogue. In both cases, the edge based representation gives the best

[1]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[2]  T. J. Stonham,et al.  Texture image classification and segmentation using RANK-order clustering , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis,.

[3]  Anil K. Jain,et al.  Markov Random Field Texture Models , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Larry S. Davis,et al.  Texture Analysis Using Generalized Co-Occurrence Matrices , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Hans-Peter Kriegel,et al.  The R*-tree: an efficient and robust access method for points and rectangles , 1990, SIGMOD '90.

[7]  Christos Faloutsos,et al.  Hilbert R-tree: An Improved R-tree using Fractals , 1994, VLDB.

[8]  Nick Roussopoulos,et al.  Nearest neighbor queries , 1995, SIGMOD '95.

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

[10]  Hugh C. Davis,et al.  Navigating from images using generic links based on image content , 1997, Electronic Imaging.

[11]  Wilson S. Geisler,et al.  Multichannel Texture Analysis Using Localized Spatial Filters , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  A. Guttmma,et al.  R-trees: a dynamic index structure for spatial searching , 1984 .

[13]  Dragutin Petkovic,et al.  Query by Image and Video Content: The QBIC System , 1995, Computer.

[14]  Mark S. Nixon,et al.  Statistical geometrical features for texture classification , 1995, Pattern Recognit..

[15]  Fang Liu,et al.  Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Anil K. Jain,et al.  A spatial filtering approach to texture analysis , 1985, Pattern Recognit. Lett..

[17]  J. Kuan,et al.  Fast k nearest neighbour search for R-tree family , 1997, Proceedings of ICICS, 1997 International Conference on Information, Communications and Signal Processing. Theme: Trends in Information Systems Engineering and Wireless Multimedia Communications (Cat..