Shape measures for image retrieval

One of the main goals in content-based image retrieval is to incorporate shape into the process in a reliable manner. In order to overcome the difficulties of directly obtaining shape information (in particular avoiding region segmentation) we develop several shape measures that tackle the problem in an indirect manner, requiring only a minimal amount of segmentation. A histogram-based scheme is then used, maintaining low complexity with high efficiency and robustness. The obtained results showed that the combination of the shape measures provide an improvement over the colour histogram.

[1]  William I. Grosky,et al.  Delaunay triangulation for image object indexing: a novel method for shape representation , 1998, Electronic Imaging.

[2]  William I. Grosky,et al.  OBJECT-BASED IMAGE RETRIEVAL USING POINT FEATURE MAPS , 1999 .

[3]  Martin C. Cooper The Tractability of Segmentation and Scene Analysis , 1998, International Journal of Computer Vision.

[4]  P. Kay Basic Color Terms: Their Universality and Evolution , 1969 .

[5]  Wen-Hsiang Tsai,et al.  Moment-preserving thresolding: A new approach , 1985, Comput. Vis. Graph. Image Process..

[6]  Cordelia Schmid,et al.  Local Grayvalue Invariants for Image Retrieval , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  John R. Smith,et al.  Image Classification and Querying Using Composite Region Templates , 1999, Comput. Vis. Image Underst..

[8]  Paul L. Rosin Measuring shape: ellipticity, rectangularity, and triangularity , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[9]  Jean-Michel Jolion,et al.  Content based image retrieval using interest points and texture features , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[10]  Ruggero Milanese,et al.  A Rotation, Translation, and Scale-Invariant Approach to Content-Based Image Retrieval , 1999, J. Vis. Commun. Image Represent..

[11]  Ramin Zabih,et al.  Comparing images using joint histograms , 1999, Multimedia Systems.

[12]  Chaomei Chen,et al.  Using CBIR and pathfinder networks for image database visualisation , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[13]  John P. Eakins,et al.  Automatic image content retrieval - are we getting anywhere? , 2002 .

[14]  Geoff A. W. West,et al.  Salience Distance Transforms , 1995, CVGIP Graph. Model. Image Process..

[15]  Anil K. Jain,et al.  Image retrieval using color and shape , 1996, Pattern Recognit..

[16]  T. John Stonham,et al.  Fuzzy Colour Category Map for Content Based Image Retrieval , 1999, BMVC.

[17]  Paul L. Rosin,et al.  Incorporating shape into histograms for CBIR , 2002, Pattern Recognit..

[18]  John Immerkær,et al.  Fast Noise Variance Estimation , 1996, Comput. Vis. Image Underst..

[19]  Michael Ian Shamos,et al.  Computational geometry: an introduction , 1985 .

[20]  Eitan M. Gurari,et al.  On the Difficulties Involved in the Segmentation of Pictures , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Shi-Kuo Chang,et al.  Iconic Indexing by 2-D Strings , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Mohan S. Kankanhalli,et al.  Shape Measures for Content Based Image Retrieval: A Comparison , 1997, Inf. Process. Manag..

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

[24]  Thomas S. Huang,et al.  Image representation and retrieval using structure features , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.