Learning Similarity for Texture Image Retrieval

A novel algorithm is proposed to learn pattern similarities for texture image retrieval. Similar patterns in different texture classes are grouped into a cluster in the feature space. Each cluster is isolated from others by an enclosed boundary, which is represented by several support vectors and their weights obtained from a statistical learning algorithm called support vector machine (SVM). The signed distance of a pattern to the boundary is used to measure its similarity. Furthermore, the patterns of different classes within each cluster are separated by several sub-boundaries, which are also learned by the SVMs. The signed distances of the similar patterns to a particular sub-boundary associated with the query image are used for ranking these patterns. Experimental results on the Brodatz texture database indicate that the new method performs significantly better than the traditional Euclidean distance based approach.

[1]  Martin Brown,et al.  Network Performance Assessment for Neurofuzzy Data Modelling , 1997, IDA.

[2]  Simone Santini,et al.  Similarity Measures , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Alex Pentland,et al.  Photobook: tools for content-based manipulation of image databases , 1994, Other Conferences.

[4]  S. Gunn.  Support Vector Machines for Classification and Regression , 1998 .

[5]  John G. Daugman,et al.  Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression , 1988, IEEE Trans. Acoust. Speech Signal Process..

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

[7]  Joachim M. Buhmann,et al.  Empirical evaluation of dissimilarity measures for color and texture , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[8]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

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

[10]  B. S. Manjunath,et al.  Texture features and learning similarity , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[12]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[13]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[14]  Massimiliano Pontil,et al.  Support Vector Machines for 3D Object Recognition , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  B. S. Manjunath,et al.  NeTra: A toolbox for navigating large image databases , 1997, Proceedings of International Conference on Image Processing.

[16]  Rosalind W. Picard,et al.  Interactive Learning Using a "Society of Models" , 2017, CVPR 1996.

[17]  R. Stephenson.  A and V , 1962, The British journal of ophthalmology.

[18]  Phil Brodatz,et al.  Textures: A Photographic Album for Artists and Designers , 1966 .

[19]  John P. Oakley,et al.  Storage and Retrieval for Image and Video Databases , 1993 .

[20]  Tom Minka,et al.  Interactive learning with a "Society of Models" , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.